68 results on '"Shape regression"'
Search Results
2. Multi-feature shape regression for face alignment
- Author
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Wei-Jong Yang, Yi-Chen Chen, Pau-Choo Chung, and Jar-Ferr Yang
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Face alignment ,Face warping ,Face recognition ,Pose variation ,Shape regression ,Telecommunication ,TK5101-6720 ,Electronics ,TK7800-8360 - Abstract
Abstract For smart living applications, personal identification as well as behavior and emotion detection becomes more and more important in our daily life. For identity classification and facial expression detection, facial features extracted from face images are the most popular and low-cost information. The face shape in terms of landmarks estimated by a face alignment method can be used for many applications including virtual face animation and real face classification. In this paper, we propose a robust face alignment method based on the multi-feature shape regression (MSR), which is evolved from the explicit shape regression (ESR) proposed in Cao et al. (Int, Vis, 2014, 107:177–190, Comput). The proposed MSR face alignment method successfully utilizes color, gradient, and regional information to increase accuracy of landmark estimation. For face recognition algorithms, we further suggest a face warping algorithm, which can cooperate with any face alignment algorithm to adjust facial pose variations to improve their recognition performances. For performance evaluations, the proposed and the existing face alignment methods are compared on the face alignment database. Based on alignment-based face recognition concept, the face alignment methods with the proposed face warping method are tested on the face database. Simulation results verify that the proposed MSR face alignment method achieves better performances than the other existing face alignment methods.
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- 2018
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3. Linear models for statistical shape analysis based on parametrized closed curves.
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Gutiérrez, Luis, Mena, Ramsés H., and Díaz-Avalos, Carlos
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LINEAR statistical models ,STATISTICS ,CURVES ,REGRESSION analysis - Abstract
We develop a simple, yet powerful, technique based on linear regression models of parametrized closed curves which induces a probability distribution on the planar shape space. Such parametrization is driven by control points which can be estimated from the data. Our proposal is capable to infer about the mean shape, to predict the shape of an object at an unobserved location, and, while doing so, to consider the effect of predictors on the shape. In particular, the model is able to detect possible differences across the levels of the predictor, thus also applicable for two-sample tests. A simple MCMC algorithm for Bayesian inference is also presented and tested with simulated and real datasets. Supplementary material is available online. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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4. Spatially Regularized Shape Analysis of the Hippocampus Using $P$-Spline Based Shape Regression.
- Author
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Achterberg, Hakim Christiaan, de Rooi, Johan J., Vernooij, Meike W., Ikram, M. Arfan, Niessen, Wiro J., Eilers, Paul H. C., and de Bruijne, Marleen
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SPLINES ,HIPPOCAMPUS (Brain) ,OLDER people ,MAGNETIC resonance ,GEOMETRIC shapes - Abstract
Shape analysis is increasingly becoming important to study changes in brain structures in relation to clinical neurological outcomes. This is a challenging task due to the high dimensionality of shape representations and the often limited number of available shapes. Current techniques counter the poor ratio between dimensions and sample size by using regularization in shape space, but do not take into account the spatial relations within the shapes. This can lead to models that are biologically implausible and difficult to interpret. We propose to use $P$ -spline based regression, which combines a generalized linear model (GLM) with the coefficients described as $B$ -splines and a penalty term that constrains the regression coefficients to be spatially smooth. Owing to the GLM, this method can naturally predict both continuous and discrete outcomes and can include non-spatial covariates without penalization. We evaluated our method on hippocampus shapes extracted from magnetic resonance (MR) images of 510 non-demented, elderly people. We related the hippocampal shape to age, memory score, and sex. The proposed method retained the good performance of current techniques, such as ridge regression, but produced smoother coefficient fields that are easier to interpret. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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5. Geodesic Regression on the Grassmannian
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Hong, Yi, Kwitt, Roland, Singh, Nikhil, Davis, Brad, Vasconcelos, Nuno, Niethammer, Marc, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fleet, David, editor, Pajdla, Tomas, editor, Schiele, Bernt, editor, and Tuytelaars, Tinne, editor
- Published
- 2014
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6. A Bayesian Approach to Statistical Shape Analysis via the Projected Normal Distribution.
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Gutiérrez, Luis, Gutiérrez-Peña, Eduardo, and Mena, Ramsés H.
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BAYESIAN analysis ,SHAPE analysis (Computational geometry) ,GAUSSIAN distribution ,REGRESSION analysis ,DISTRIBUTION (Probability theory) - Published
- 2019
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7. Recurrent Shape Regression.
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Cui, Zhen, Xiao, Shengtao, Niu, Zhiheng, Yan, Shuicheng, and Zheng, Wenming
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COMPUTER vision , *REGRESSION analysis , *SIMULATION methods & models , *IMAGE processing , *NUMERICAL analysis - Abstract
An end-to-end network architecture, the Recurrent Shape Regression (RSR), is presented to deal with the task of facial shape detection, a crucial step in many computer vision problems. The RSR generalizes the conventional cascaded regression into a recurrent dynamic network through abstracting common latent models with stage-to-stage operations. Instead of invariant regression transformation, we construct shape-dependent dynamic regressors to attain the recurrence of regression action itself. The regressors can be stacked into a high-order regression network to represent more complex shape regression. By further integrating feature learning as well as global shape constraint, the RSR becomes more controllable in entire optimization of shape regression, where the gradient computation can be efficiently back-propagated through time. To handle the possible partial occlusions of shapes, we propose a mimic virtual occlusion strategy by randomly disturbing certain point cliques without the requirement of any annotations of occlusion information or even occluded training data. Extensive experiments on five face datasets demonstrate that the proposed RSR outperforms the recent state-of-the-art cascaded approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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8. Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis.
- Author
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Chandran, Vimal, Maquer, Ghislain, Gerig, Thomas, Zysset, Philippe, and Reyes, Mauricio
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CANCELLOUS bone , *COMPACT bone , *COMPUTED tomography , *MORPHOMETRICS , *GAUSSIAN processes - Abstract
Highlights • A bone shape regression produces surface meshes for cortical and trabecular bone. • Local corrections are done based on supervised learning between QCT and HRpQCT images. • For FEA purposes, those corrections are regularised by Gaussian process model. Cortical thickness is automatically estimated with high accuracy without segmentation. Graphical abstract Abstract Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a three-steps approach is proposed. 1) Two initial surface meshes approximating the outer and inner cortical surfaces are generated via a shape regression based on morphometric features and statistical shape model parameters. 2) The meshes are then corrected locally using a supervised learning model build from image features extracted from pairs of QCT (0.3-1 mm resolution) and HRpQCT images (82 µm resolution). As the resulting meshes better follow the cortical surfaces, the cortical thickness can be estimated at sub-voxel precision. 3) The meshes are finally regularized by a Gaussian process model featuring a two-kernel model, which seamlessly enables smoothness and shape-awareness priors during regularization. The resulting meshes yield high-quality mesh element properties, suitable for construction of tetrahedral meshes and finite element simulations. This pipeline was applied to 36 pairs of proximal femurs (17 males, 19 females, 76 ± 12 years) scanned under QCT and HRpQCT modalities. On a set of leave-one-out experiments, we quantified accuracy (root mean square error = 0.36 ± 0.29 mm) and robustness (Hausdorff distance = 3.90 ± 1.57 mm) of the outer surface meshes. The error in the estimated cortical thickness (0.05 ± 0.40 mm), and the tetrahedral mesh quality (aspect ratio = 1.4 ± 0.02) are also reported. The proposed pipeline produces finite element meshes with patient-specific bone shape and sub-voxel cortical thickness directly from CT scans. It also ensures that the nodes and elements numbering remains consistent and independent of the morphology, which is a distinct advantage in population studies. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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9. Recurrent Convolutional Shape Regression.
- Author
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Wang, Wei, Tulyakov, Sergey, and Sebe, Nicu
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REGRESSION analysis , *IMAGE recognition (Computer vision) , *VISION , *ARTIFICIAL neural networks , *COMPUTER storage devices - Abstract
The mainstream direction in face alignment is now dominated by cascaded regression methods. These methods start from an image with an initial shape and build a set of shape increments based on features with respect to the current estimated shape. These shape increments move the initial shape to the desired location. Despite the advantages of the cascaded methods, they all share two major limitations: (i) shape increments are learned independently from each other in a cascaded manner, (ii) the use of standard generic computer vision features such SIFT, HOG, does not allow these methods to learn problem-specific features. In this work, we propose a novel Recurrent Convolutional Shape Regression (RCSR) method that overcomes these limitations. We formulate the standard cascaded alignment problem as a recurrent process and learn all shape increments jointly, by using a recurrent neural network with a gated recurrent unit. Importantly, by combining a convolutional neural network with a recurrent one we avoid hand-crafted features, widely adopted in the literature and thus we allow the model to learn task-specific features. Besides, we employ the convolutional gated recurrent unit which takes as input the feature tensors instead of flattened feature vectors. Therefore, the spatial structure of the features can be better preserved in the memory of the recurrent neural network. Moreover, both the convolutional and the recurrent neural networks are learned jointly. Experimental evaluation shows that the proposed method has better performance than the state-of-the-art methods, and further supports the importance of learning a single end-to-end model for face alignment. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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10. Geodesic Shape Regression in the Framework of Currents
- Author
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Fishbaugh, James, Prastawa, Marcel, Gerig, Guido, Durrleman, Stanley, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gee, James C., editor, Joshi, Sarang, editor, Pohl, Kilian M., editor, Wells, William M., editor, and Zöllei, Lilla, editor
- Published
- 2013
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11. New Directional Residuals to Treat Shape Changes Using Spherical Regression Models
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Moghimbeygi, Meisam and Golalizadeh, Mousa
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- 2020
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12. Geodesic shape regression with multiple geometries and sparse parameters.
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Fishbaugh, James, Durrleman, Stanley, Prastawa, Marcel, and Gerig, Guido
- Subjects
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MAGNETIC resonance imaging of the brain , *GEODESIC distance , *SPARSE approximations , *CHILD development , *CLOUD computing , *SPATIOTEMPORAL processes - Abstract
Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination. In this paper, we present a geodesic regression model in the large deformation (LDDMM) framework applicable to multi-object complexes in a variety of shape representations. Our model decouples the deformation parameters from the specific shape representations, allowing the complexity of the model to reflect the nature of the shape changes, rather than the sampling of the data. As a consequence, the sparse representation of diffeomorphic flow allows for the straightforward embedding of a variety of geometry in different combinations, which all contribute towards the estimation of a single deformation of the ambient space. Additionally, the sparse representation along with the geodesic constraint results in a compact statistical model of shape change by a small number of parameters defined by the user. Experimental validation on multi-object complexes demonstrate robust model estimation across a variety of parameter settings. We further demonstrate the utility of our method to support the analysis of derived shape features, such as volume, and explore shape model extrapolation. Our method is freely available in the software package deformetrica which can be downloaded at www.deformetrica.org . [ABSTRACT FROM AUTHOR]
- Published
- 2017
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13. Longitudinal modeling of appearance and shape and its potential for clinical use.
- Author
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Gerig, Guido, Fishbaugh, James, and Sadeghi, Neda
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DISEASE progression , *DEGENERATION (Pathology) , *SPATIOTEMPORAL processes , *DIAGNOSTIC imaging , *MEDICAL innovations - Abstract
Clinical assessment routinely uses terms such as development, growth trajectory, degeneration, disease progression, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that single measurements in time and cross-sectional comparison may not sufficiently describe spatiotemporal changes. In view of medical imaging, such tasks encourage subject-specific longitudinal imaging. Whereas follow-up, monitoring and prediction are natural tasks in clinical diagnosis of disease progression and of assessment of therapeutic intervention, translation of methodologies for calculation of temporal profiles from longitudinal data to clinical routine still requires significant research and development efforts. Rapid advances in image acquisition technology with significantly reduced acquisition times and with increase of patient comfort favor repeated imaging over the observation period. In view of serial imaging ranging over multiple years, image acquisition faces the challenging issue of scanner standardization and calibration which is crucial for successful spatiotemporal analysis. Longitudinal 3D data, represented as 4D images, capture time-varying anatomy and function. Such data benefits from dedicated analysis methods and tools that make use of the inherent correlation and causality of repeated acquisitions of the same subject. Availability of such data spawned progress in the development of advanced 4D image analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. This paper provides examples of recently developed analysis methodologies for 4D image data, primarily focusing on progress in areas of core expertise of the authors. These include spatiotemporal shape modeling and growth trajectories of white matter fiber tracts demonstrated with examples from ongoing longitudinal clinical neuroimaging studies such as analysis of early brain growth in subjects at risk for mental illness and neurodegeneration in Huntington’s disease (HD). We will discuss broader aspects of current limitations and need for future research in view of data consistency and analysis methodologies. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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14. Robust Video Text Detection Through Parametric Shape Regression, Propagation and Fusion
- Author
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Feng Su, Long Chen, and Jiahao Shi
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Fusion ,business.industry ,Computer science ,Shape regression ,Pattern recognition ,Artificial intelligence ,Text detection ,business ,Parametric statistics - Published
- 2021
15. Machine Learning towards General Medical Image Segmentation
- Author
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Clara Tam
- Subjects
machine learning ,segmentation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,deep learning ,shape regression ,multiapplication ,multitask learning ,Biomedical Engineering and Bioengineering ,Other Analytical, Diagnostic and Therapeutic Techniques and Equipment - Abstract
The quality of patient care associated with diagnostic radiology is proportionate to a physician's workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object's contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net's performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency.
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- 2020
16. 3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View
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Bernd G. Pfrommer, Marc A. Badger, Adarsh Modh, Marc F. Schmidt, Kostas Daniilidis, Ammon Perkes, Nikos Kolotouros, and Yufu Wang
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FOS: Computer and information sciences ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Article ,Mesh model ,03 medical and health sciences ,0202 electrical engineering, electronic engineering, information engineering ,Code (cryptography) ,Computer vision ,Pose ,030304 developmental biology ,ComputingMethodologies_COMPUTERGRAPHICS ,0303 health sciences ,business.industry ,Shape regression ,020207 software engineering ,Social cue ,Pipeline (software) ,Single view ,Social animal ,I.4.8 ,Artificial intelligence ,business - Abstract
Automated capture of animal pose is transforming how we study neuroscience and social behavior. Movements carry important social cues, but current methods are not able to robustly estimate pose and shape of animals, particularly for social animals such as birds, which are often occluded by each other and objects in the environment. To address this problem, we first introduce a model and multi-view optimization approach, which we use to capture the unique shape and pose space displayed by live birds. We then introduce a pipeline and experiments for keypoint, mask, pose, and shape regression that recovers accurate avian postures from single views. Finally, we provide extensive multi-view keypoint and mask annotations collected from a group of 15 social birds housed together in an outdoor aviary. The project website with videos, results, code, mesh model, and the Penn Aviary Dataset can be found at https://marcbadger.github.io/avian-mesh., Comment: In ECCV 2020
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- 2020
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17. A repeatable geometric morphometric approach to the analysis of hand entheseal three-dimensional form
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Fotios Alexandros Karakostis, Heike Scherf, Katerina Harvati, Gerhard Hotz, and Joachim Wahl
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Adult ,Male ,0106 biological sciences ,Physical activity ,Biology ,010603 evolutionary biology ,01 natural sciences ,Anthropology, Physical ,Imaging, Three-Dimensional ,Humans ,0601 history and archaeology ,Musculoskeletal System ,Orthodontics ,060101 anthropology ,Anthropometry ,Shape regression ,Reproducibility of Results ,06 humanities and the arts ,Hand ,Enthesis ,Bone length ,Anthropology ,Regression Analysis ,Allometry ,Anatomic Landmarks ,Anatomy - Abstract
Objectives The purpose of this study was to put forth a precise landmark-based technique for reconstructing the three-dimensional shape of human entheseal surfaces, to investigate whether the shape of human entheses is related to their size. The effects of age-at-death and bone length on entheseal shapes were also assessed. Materials and methods The sample comprised high-definition three-dimensional models of three right hand entheseal surfaces, which correspond to 45 male adult individuals of known age. For each enthesis, a particular landmark configuration was introduced, whose precision was tested both within and between observers. The effect of three-dimensional size, age-at-death, and bone length on shape was investigated through shape regression. Results The method presented high intra-observer and inter-observer repeatability. All entheses showed significant allometry, with the area of opponens pollicis demonstrating the most substantial relationship. This was particularly due to variation related to its proximal elongated ridge. The effect of age-at-death and bone length on entheses was limited. Discussion The introduced methodology can set a reliable basis for further research on the factors affecting entheseal shape. Using both size and shape, variables can provide further information on entheseal variation and its biomechanical implications. The low entheseal variation by age verifies that specimens under 50 years of age are not substantially affected by age-related changes. The lack of correlation between entheseal shape and bone length or age implies that other factors may regulate entheseal surfaces. Future research should focus on multivariate shape patterns among entheses and their association with occupation.
- Published
- 2018
18. Association of body shape with amount of Arabian genetic contribution in the Lipizzan horse
- Author
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Gertrud Grilz-Seger, Michaela Horna, Gottfried Brem, Maximilian Dobretsberger, and Thomas Druml
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0301 basic medicine ,Cultural Studies ,Animal breeding ,Zoology ,Introgression ,Biology ,Crossbreed ,lcsh:Agriculture ,03 medical and health sciences ,stomatognathic system ,lcsh:Zoology ,Arabian horse ,lcsh:QL1-991 ,lcsh:Science ,lcsh:SF1-1100 ,0402 animal and dairy science ,Religious studies ,Shape regression ,lcsh:S ,Horse ,04 agricultural and veterinary sciences ,040201 dairy & animal science ,humanities ,030104 developmental biology ,Backcrossing ,lcsh:Q ,Gene pool ,lcsh:Animal culture - Abstract
Crossbreeding between individuals of different breeds and introgression, the transfer of genes between breeds and/or populations mediated primarily by backcrossing, have been characteristic tools used in the refinement or optimisation of practical horse breeding. In this study we analysed the genetic contribution of the Arabian horse to the gene pool of the Lipizzan horse and its association with the overall type via shape regression analysis in 158 Lipizzan horses from the Austrian federal stud farm of Piber and the Spanish Riding School. Although crossbreeding with Arabian horses took place between 1776 and 1945, we found a significant association between Lipizzan body shape (p
- Published
- 2018
19. Toward a Comprehensive Framework for the Spatiotemporal Statistical Analysis of Longitudinal Shape Data.
- Author
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Durrleman, Stanley, Pennec, Xavier, Trouvé, Alain, Braga, José, Gerig, Guido, and Ayache, Nicholas
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SPATIOTEMPORAL processes , *LONGITUDINAL method , *SCALAR field theory , *IMAGE databases , *NEUROSCIENCES , *HIPPOCAMPUS (Brain) , *AUTISM in children - Abstract
This paper proposes an original approach for the statistical analysis of longitudinal shape data. The proposed method allows the characterization of typical growth patterns and subject-specific shape changes in repeated time-series observations of several subjects. This can be seen as the extension of usual longitudinal statistics of scalar measurements to high-dimensional shape or image data. The method is based on the estimation of continuous subject-specific growth trajectories and the comparison of such temporal shape changes across subjects. Differences between growth trajectories are decomposed into morphological deformations, which account for shape changes independent of the time, and time warps, which account for different rates of shape changes over time. Given a longitudinal shape data set, we estimate a mean growth scenario representative of the population, and the variations of this scenario both in terms of shape changes and in terms of change in growth speed. Then, intrinsic statistics are derived in the space of spatiotemporal deformations, which characterize the typical variations in shape and in growth speed within the studied population. They can be used to detect systematic developmental delays across subjects. In the context of neuroscience, we apply this method to analyze the differences in the growth of the hippocampus in children diagnosed with autism, developmental delays and in controls. Result suggest that group differences may be better characterized by a different speed of maturation rather than shape differences at a given age. In the context of anthropology, we assess the differences in the typical growth of the endocranium between chimpanzees and bonobos. We take advantage of this study to show the robustness of the method with respect to change of parameters and perturbation of the age estimates. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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20. Automated land use classification: Supervised segmentation of road structures on aerial images using shape regression
- Author
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Brand, Patrick (author) and Brand, Patrick (author)
- Abstract
Recent advances in Artificial Intelligence and Computer Vision have been showed to be promising for automated land use classification of remotely sensed data. However, current state-of-the-art per-pixel segmentation networks fail to accurately capture geometrical and topological properties on land use segmentation, as these methods have inherently a lot of freedom. These geometrical and topological properties of land use structures are crucial for describing land usage on topographical maps, as their purpose is to present insight into topology and borders of land use structures. In order to preserve the geometrical and topological properties of land use structures, a novel segmentation method is introduced and tested on road structures in this thesis. Unlike current state-of-the-art segmentation networks, this new method performs the segmentation task by utilizing shape regression techniques as currently applied by state-of-the-art object detection networks. As modern object detection methods are only able to perform regression on simplistic shapes, and road structures generally describe complex shapes, a new topology preserving annotation generation method is introduced that subdivides a complex road structure into a set of oriented rectangular shapes. Since not many publicly available land use datasets contain both aerial images and per-pixel annotations, a new dataset based on aerial images and land use annotations, covering large areas of the Netherlands, is introduced as well. The results show that the novel segmentation method is capable of learning the newly introduced road structure representation, which preserves geometrical and topological properties. The connectedness property, however, is lost. The novel method does currently not outperform current state-of-the-art per-pixel segmentation networks, although several directions for future work are proposed to improve the segmentation performance of the shape regression based technique and preserve the conne, Computer Science
- Published
- 2019
21. Comparison of the endocranial ontogenies between chimpanzees and bonobos via temporal regression and spatiotemporal registration
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Durrleman, Stanley, Pennec, Xavier, Trouvé, Alain, Ayache, Nicholas, and Braga, José
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ONTOGENY , *CHIMPANZEES , *BONOBO , *REGRESSION (Civilization) , *DENTAL anthropology , *ESTIMATES - Abstract
Abstract: This paper aims at quantifying ontogenetic differences between bonobo (Pan paniscus) and chimpanzee (Pan troglodytes) endocrania, using dental development as a timeline. We utilize a methodology based on smooth and invertible deformations combined with a metric of “currents” that defines a distance between endocranial surfaces and does not rely on correspondence between landmarks. This allows us to perform a temporal surface regression that estimates typical endocranial ontogenetic trajectories separately for bonobos and chimpanzees. We highlight non-linear patterns of endocranial ontogenetic change and significant differences between species at local anatomical levels rather than considering the endocranium as a uniform entity. A spatiotemporal registration permits the quantification of inter-species differences decomposed into a morphological deformation (accounting for size and shape differences independently of age) and a time warp (accounting for changes in the dynamics of development). Our statistical simulations suggest that patterns of endocranial volume (EV) increase may differ significantly between bonobos and chimpanzees, with an earlier phase of a relatively rapid increase (preferentially at some endocranial subdivisions) in the former and a much later phase of relatively rapid increase in the latter. As a consequence, the chimpanzee endocranium appears to reach its adult size later. Moreover, the time warp indicates that juvenile bonobos develop much slower than juvenile chimpanzees, suggesting that inter-specific ontogenetic shifts do not only concern EV increase, but also the rate of shape changes over time. Our method provides, for the first time, a quantitative estimation of inter-specific ontogenetic shifts that appear to differentiate non-linearly. [Copyright &y& Elsevier]
- Published
- 2012
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22. Generating shapes by analogies: An application to hearing aid design
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Unal, Gozde, Nain, Delphine, Slabaugh, Greg, and Fang, Tong
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THREE-dimensional imaging , *MATHEMATICAL models , *HEARING aid design & construction , *GEOMETRIC shapes , *PARAMETER estimation , *REGRESSION analysis , *STATISTICS - Abstract
Abstract: 3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to the estimation of customized hearing aid devices. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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23. Quantitative vertebral morphometry using neighbor-conditional shape models
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de Bruijne, Marleen, Lund, Michael T., Tankó, László B., Pettersen, Paola C., and Nielsen, Mads
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PHYSICIANS , *SKELETON , *RADIOGRAPHY , *RADIOLOGISTS - Abstract
Abstract: A novel method for vertebral fracture quantification from X-ray images is presented. Using pairwise conditional shape models trained on a set of healthy spines, the most likely normal vertebra shapes are estimated conditional on the shapes of all other vertebrae in the image. The difference between the true shape and the reconstructed normal shape is subsequently used as a measure of abnormality. In contrast with the current (semi-)quantitative grading strategies this method takes the full shape into account, it develops a patient-specific reference by combining population-based information on biological variation in vertebral shape and vertebra interrelations, and it provides a continuous measure of deformity. The method is demonstrated on 282 lateral spine radiographs with in total 93 fractures. Vertebral fracture detection is shown to be in good agreement with semi-quantitative scoring by experienced radiologists and is superior to the performance of shape models alone. [Copyright &y& Elsevier]
- Published
- 2007
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24. MSR: Multi-Scale Shape Regression for Scene Text Detection
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Wei Zhang, Chuhui Xue, and Shijian Lu
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FOS: Computer and information sciences ,Quadrilateral ,Scale (ratio) ,business.industry ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,Shape regression ,Boundary (topology) ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Text detection ,Variation (game tree) ,Scale variation ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Segmentation ,Artificial intelligence ,business - Abstract
State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations., Comment: Accepted by IJCAI19
- Published
- 2019
25. A Bayesian Approach to Statistical Shape Analysis via the Projected Normal Distribution
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Luis Gutiérrez, Ramsés H. Mena, and Eduardo Gutiérrez-Peña
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Statistics and Probability ,Scale (ratio) ,Computer science ,Applied Mathematics ,Statistical shape analysis ,05 social sciences ,Bayesian probability ,shape regression ,identifiability ,01 natural sciences ,Bookstein coordinates ,Normal distribution ,Data set ,medical image ,010104 statistics & probability ,62J05 ,0502 economics and business ,Identifiability ,Configuration space ,0101 mathematics ,62F15 ,62H35 ,Algorithm ,Rotation (mathematics) ,050205 econometrics - Abstract
This work presents a Bayesian predictive approach to statistical shape analysis. A modeling strategy that starts with a Gaussian distribution on the configuration space, and then removes the effects of location, rotation and scale, is studied. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows us to generalize the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.
- Published
- 2019
26. Model selection for spatiotemporal modeling of early childhood sub-cortical development
- Author
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James Fishbaugh, Veronica Murphy, John H. Gilmore, Beatriz Paniagua, Martin Styner, Mahmoud Mostapha, and Guido Gerig
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Flexibility (engineering) ,Shape change ,Computer science ,business.industry ,Model selection ,Shape regression ,Machine learning ,computer.software_genre ,Missing data ,Article ,Variety (cybernetics) ,Sensitivity (control systems) ,Artificial intelligence ,Early childhood ,business ,computer - Abstract
Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not over fit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation.
- Published
- 2019
27. Fitting Convex Sets to Data: Algorithms and Applications
- Author
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Soh, Yong Sheng
- Subjects
Convex Optimization ,Semidefinite Programming ,Representation Learning ,Atomic Norm ,Applied And Computational Mathematics ,Shape Regression - Abstract
This thesis concerns the geometric problem of finding a convex set that best fits a given dataset. Our question serves as an abstraction for data-analytical tasks arising in a range of scientific and engineering applications. We focus on two specific instances: 1. A key challenge that arises in solving inverse problems is ill-posedness due to a lack of measurements. A prominent family of methods for addressing such issues is based on augmenting optimization-based approaches with a convex penalty function so as to induce a desired structure in the solution. These functions are typically chosen using prior knowledge about the data. In Chapter 2, we study the problem of learning convex penalty functions directly from data for settings in which we lack the domain expertise to choose a penalty function. Our solution relies on suitably transforming the problem of learning a penalty function into a fitting task. 2. In Chapter 3, we study the problem of fitting tractably-described convex sets given the optimal value of linear functionals evaluated in different directions. Our computational procedures for fitting convex sets are based on a broader framework in which we search among families of sets that are parameterized as linear projections of a fixed structured convex set. The utility of such a framework is that our procedures reduce to the computation of simple primitives at each iteration, and these primitives can be further performed in parallel. In addition, by choosing structured sets that are non-polyhedral, our framework provides a principled way to search over expressive collections of non-polyhedral descriptions; in particular, convex sets that can be described via semidefinite programming provide a rich source of non-polyhedral sets, and such sets feature prominently in this thesis. We provide performance guarantees for our procedures. Our analyses rely on understanding geometrical aspects of determinantal varieties, building on ideas from empirical processes as well as random matrix theory. We demonstrate the utility of our framework with numerical experiments on synthetic data as well as applications in image denoising and computational geometry. As secondary contributions, we consider the following: 1. In Chapter 4, we consider the problem of optimally approximating a convex set as a spectrahedron of a given size. Spectrahedra are sets that can be expressed as feasible regions of a semidefinite program. 2. In Chapter 5, we consider change-point estimation in a sequence of high-dimensional signals given noisy observations. Our method integrates classical approaches with a convex optimization-based step that is useful for exploiting structure in high-dimensional data.
- Published
- 2019
- Full Text
- View/download PDF
28. Real-time facial animation on mobile devices.
- Author
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Weng, Yanlin, Cao, Chen, Hou, Qiming, and Zhou, Kun
- Subjects
REAL-time control ,COMPUTER-generated imagery ,SYSTEMS design ,WEBCAMS ,REGRESSION analysis ,DATA analysis - Abstract
Abstract: We present a performance-based facial animation system capable of running on mobile devices at real-time frame rates. A key component of our system is a novel regression algorithm that accurately infers the facial motion parameters from 2D video frames of an ordinary web camera. Compared with the state-of-the-art facial shape regression algorithm [1], which takes a two-step procedure to track facial animations (i.e., first regressing the 3D positions of facial landmarks, and then computing the head poses and expression coefficients), we directly regress the head poses and expression coefficients. This one-step approach greatly reduces the dimension of the regression target and significantly improves the tracking performance while preserving the tracking accuracy. We further propose to collect the training images of the user under different lighting environments, and make use of the data to learn a user-specific regressor, which can robustly handle lighting changes that frequently occur when using mobile devices. [Copyright &y& Elsevier]
- Published
- 2014
- Full Text
- View/download PDF
29. Multi-feature shape regression for face alignment
- Author
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Yang, Wei-Jong, Chen, Yi-Chen, Chung, Pau-Choo, and Yang, Jar-Ferr
- Published
- 2018
- Full Text
- View/download PDF
30. Multi-feature shape regression for face alignment
- Author
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Yi Chen Chen, Pau-Choo Chung, Jar-Ferr Yang, and Wei Jong Yang
- Subjects
Face warping ,Facial expression ,Computer science ,business.industry ,lcsh:Electronics ,lcsh:TK7800-8360 ,Pose variation ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Animation ,Shape regression ,Facial recognition system ,lcsh:Telecommunication ,Identification (information) ,lcsh:TK5101-6720 ,Face (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,Identity (object-oriented programming) ,020201 artificial intelligence & image processing ,Artificial intelligence ,Face recognition ,Image warping ,business ,Face alignment - Abstract
For smart living applications, personal identification as well as behavior and emotion detection becomes more and more important in our daily life. For identity classification and facial expression detection, facial features extracted from face images are the most popular and low-cost information. The face shape in terms of landmarks estimated by a face alignment method can be used for many applications including virtual face animation and real face classification. In this paper, we propose a robust face alignment method based on the multi-feature shape regression (MSR), which is evolved from the explicit shape regression (ESR) proposed in Cao et al. (Int, Vis, 2014, 107:177–190, Comput). The proposed MSR face alignment method successfully utilizes color, gradient, and regional information to increase accuracy of landmark estimation. For face recognition algorithms, we further suggest a face warping algorithm, which can cooperate with any face alignment algorithm to adjust facial pose variations to improve their recognition performances. For performance evaluations, the proposed and the existing face alignment methods are compared on the face alignment database. Based on alignment-based face recognition concept, the face alignment methods with the proposed face warping method are tested on the face database. Simulation results verify that the proposed MSR face alignment method achieves better performances than the other existing face alignment methods.
- Published
- 2018
31. 4D continuous medial representation by geodesic shape regression
- Author
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James Fishbaugh, Sungmin Hong, and Guido Gerig
- Subjects
Geodesic ,business.industry ,Computer science ,Shape regression ,Medial representation ,Pattern recognition ,Article ,Ambient space ,sense organs ,Artificial intelligence ,Diffeomorphism ,Intrinsic geometry ,skin and connective tissue diseases ,business ,Shape analysis (digital geometry) - Abstract
Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes. To avoid the traditional step of aligning individual objects, shape changes are modeled via diffeomorphic ambient space deformations. Using a medial shape representation, we separately capture object pose changes and intrinsic geometry changes. Tests and validation with synthetic and real anatomical shapes demonstrate that the new method captures extrinsic shape changes as well as intrinsic shape changes encoded with CM-Reps, a highly relevant property for studying growth and disease processes.
- Published
- 2018
32. Adaptively Designed Shape Regression Model for Facial Point Detection
- Author
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Yasue Mitsukura and Koichi Takahashi
- Subjects
business.industry ,Computer science ,Media Technology ,Shape regression ,Point (geometry) ,Computer vision ,Pattern recognition ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Computer Science Applications - Published
- 2015
33. A robust submap-based road shape estimation via iterative Gaussian process regression
- Author
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Jianru Xue, Di Wang, Dixiao Cui, and Yang Zhong
- Subjects
050210 logistics & transportation ,Safe driving ,business.industry ,Computer science ,05 social sciences ,Shape regression ,01 natural sciences ,Spline (mathematics) ,symbols.namesake ,Kriging ,Active shape model ,0502 economics and business ,0103 physical sciences ,symbols ,Computer vision ,Artificial intelligence ,business ,010303 astronomy & astrophysics ,Gaussian process ,Algorithm - Abstract
Road shape estimation is important for the safe driving of intelligent vehicles. The common road shape models such as line/parabola, spline and clothoid are lacking of flexibility in various urban traffic scenes. In this paper, a robust road shape model which consists of multiple overlapped submaps is proposed. Each individual submap is represented by a smooth curve generated through Gaussian process(GP). To estimate parameters of a GP submap, a framework involving pre-processing, pose correction, road shape regression and map updating/creating is proposed. Pose correction is achieved by fusion of vehicle motion model and simplified GP-based observation model. Road shape regression is used to extract a coarse road shape. Map updating/creating is used to adapt to the new coming data and generates refined road shape. A robust iterative Gaussian process regression(iGPR) is utilized in both road shape regression and map updating/creating. Extensive experimental results show the efficiency of the proposed method.
- Published
- 2017
34. Geodesic shape regression with multiple geometries and sparse parameters
- Author
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Guido Gerig, Marcel Prastawa, Stanley Durrleman, James Fishbaugh, Scientific Computing and Imaging Institute (SCI Institute), University of Utah, Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris
- Subjects
Geodesic ,Spatiotemporal ,[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,[SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology ,Health Informatics ,02 engineering and technology ,Shape regression ,Article ,03 medical and health sciences ,0302 clinical medicine ,Imaging, Three-Dimensional ,Active shape model ,0202 electrical engineering, electronic engineering, information engineering ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Humans ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Mathematics ,Ultrasonography ,Internet ,Models, Statistical ,Radiological and Ultrasound Technology ,business.industry ,4D shape modeling ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Statistical model ,Heart ,Sparse approximation ,Computer Graphics and Computer-Aided Design ,Magnetic Resonance Imaging ,3. Good health ,Ambient space ,Multi-object complex ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Embedding ,Regression Analysis ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Principal geodesic analysis ,business ,Algorithm ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,030217 neurology & neurosurgery ,LDDMM ,Algorithms ,Software ,Shape analysis (digital geometry) - Abstract
International audience; Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination. In this paper, we present a geodesic regression model in the large deformation (LDDMM) framework applicable to multi-object complexes in a variety of shape representations. Our model decouples the deformation parameters from the specific shape representations, allowing the complexity of the model to reflect the nature of the shape changes, rather than the sampling of the data. As a consequence, the sparse representation of diffeomorphic flow allows for the straightforward embedding of a variety of geometry in different combinations, which all contribute towards the estimation of a single deformation of the ambient space. Additionally, the sparse representation along with the geodesic constraint results in a compact statistical model of shape change by a small number of parameters defined by the user. Experimental validation on multi-object complexes demonstrate robust model estimation across a variety of parameter settings. We further demonstrate the utility of our method to support the analysis of derived shape features, such as volume, and explore shape model extrapolation. Our method is freely available in the software package deformetrica which can be downloaded at www.deformetrica.org.
- Published
- 2017
- Full Text
- View/download PDF
35. A joint facial point detection method of deep convolutional network and shape regression
- Author
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Tangqin Yang, Ning Zhou, and Chang Shu
- Subjects
Training set ,Computer science ,business.industry ,Feature extraction ,0202 electrical engineering, electronic engineering, information engineering ,Shape regression ,Initialization ,020207 software engineering ,020201 artificial intelligence & image processing ,Pattern recognition ,02 engineering and technology ,Artificial intelligence ,business - Abstract
Facial landmark detection is a challenging task with broad applications. Many approaches have been proposed with varying degrees of success. Regression based methods update the facial point positions iteratively. The mean shape or shapes sampled from training set is often used as the initialization, which sometimes may lead to a local minimum in update due to the offset of initial positions and target positions. On the other hand, convolution network based method shows superior accuracy, at the cost of a complex and unwieldy architecture of deep model. To address the above limitations, a new approach for facial landmark detection is proposed in this paper. The key idea is to combine deep convolution networks with shape regression approach. Deep convolution networks in the first level provide a highly robust initial shape, while the following regression finely tunes the initial prediction to achieve high accuracy. Extensive experiments show that the regression based methods are very sensitive to initializations and the proposed approach (i) achieves high accuracy on public datasets, especially outperforms existing methods in challenging conditions like large pose expression variation, and (ii) reduces model complexity drastically compared to previous methods.
- Published
- 2016
36. Face Alignment by Explicit Shape Regression
- Author
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Fang Wen, Yichen Wei, Jian Sun, and Xudong Cao
- Subjects
Training set ,business.industry ,Shape regression ,Regression analysis ,Pattern recognition ,Feature selection ,Facial recognition system ,Regression ,Image (mathematics) ,Correlation ,Set (abstract data type) ,Artificial Intelligence ,Active shape model ,Face (geometry) ,Pattern recognition (psychology) ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Software ,Parametric statistics ,Mathematics - Abstract
We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.
- Published
- 2013
37. 3D shape regression for real-time facial animation
- Author
-
Yanlin Weng, Kun Zhou, Chen Cao, and Stephen Lin
- Subjects
Sequence ,Face hallucination ,business.industry ,Facial motion capture ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Shape regression ,Computer Graphics and Computer-Aided Design ,Set (abstract data type) ,Face (geometry) ,Computer vision ,Artificial intelligence ,business ,Computer facial animation ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.
- Published
- 2013
38. Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression
- Author
-
Rahil Garnavi, Pallab Roy, Dwarikanath Mahapatra, and Suman Sedai
- Subjects
Statistics::Theory ,Computer science ,business.industry ,Fundus image ,Shape regression ,Retinal ,Pattern recognition ,Optic cup (anatomical) ,Feedback loop ,Regression ,Statistics::Machine Learning ,chemistry.chemical_compound ,medicine.anatomical_structure ,chemistry ,Computer Science::Computer Vision and Pattern Recognition ,medicine ,Statistics::Methodology ,Segmentation ,Artificial intelligence ,business ,Optic disc - Abstract
Accurate segmentation of optic cup and disc in retinal fundus images is required to derive the cup-to-disc ratio (CDR) parameter which is the main indicator for Glaucoma assessment. In this paper, we propose a coupled regression method for accurate segmentation of optic cup and disc in retinal colour fundus image. The proposed coupled regression framework consists of a parameter regressor which directly predicts CDR from a given image, as well as an ensemble shape regressor which iteratively estimates the OD-OC boundary by taking into account the CDR estimated by the parameter regressor. The parameter regressor and the shape regressor are then coupled together within a feedback loop so that estimation of one reinforces the other. Both parameter regressor and the ensemble shape regressor are modeled using Boosted Regression Trees. The proposed optic cup and disc segmentation method is applied on an image set of 50 patients and demonstrates high segmentation accuracy. A comparative study shows that our proposed method outperforms state of the art methods for cup segmentation.
- Published
- 2016
39. High-quality initial shape estimation for cascade shape regression
- Author
-
Lizhuang Ma, Yangyang Hao, Hengliang Zhu, and Kai Wu
- Subjects
Boosting (machine learning) ,business.industry ,Shape regression ,Binary number ,Initialization ,Pattern recognition ,02 engineering and technology ,Random forest ,Square error ,Cascade ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Invariant (mathematics) ,business ,Mathematics - Abstract
Cascade shape regression has been proven to be an accurate, robust and fast framework for face alignment. Recently, a lot of methods based on this framework have emerged which focus on boosting learning method or extracting geometric invariant features. Despite the great success of these methods, none of them are initialization independent, which limits their prediction performance to some complex face shapes. In this paper, we propose a novel initialization scheme called high-quality initial shape estimation to generate high-quality initial face shapes. First, we extract Gabor features to represent facial appearance. Then we minimize the square error between the target shapes and the estimated initial shapes using a random regression forest and binary comparison features. Finally, we use a standard cascade shape regressor to regress the estimated initial shape for robust face alignment. Experimental results show that our method achieves state-of-the-art performance on the 300-W dataset, which is the most challenging dataset today.
- Published
- 2016
40. Alignment of a Point Distribution Model onto the human body for person re-identification
- Author
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Huynh, Olivier, Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Université Paris sciences et lettres, and Philippe Fuchs
- Subjects
Human Body ,Modèle à Distribution de Points ,Alignement ,Point Distribution Model ,Re-Identification ,Embedded applications ,Régression de Forme ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Ré-Identification ,Applications embarquées ,Alignment ,Shape Regression ,Corps humain - Abstract
The emergence of mobile systems brings new problematics in computer vision. Static camera-based methods for re-identification need to be adapted in this new context. To deal with dynamical background, this thesis proposes to employ the well known Point Distribution Model (PDM), usually applied for face alignment, on the human body. Three advantages come from this pre-processing before re-identification, segment the person from background, enhance robustness to the person pose and extract spatial key points to build a behavioural-based signature.We implement and evaluate a complete framework for re-identification, divided in three sequential modules. The first one corresponds to the pedestrian detection. We use an efficient method of the state of the art employing the Channel Features with the algorithm AdaBoost.The second one is the PDM alignment within the bounding box provided by the detection step. Two distinct approaches are presented in this thesis. The first method relies on a parametric formulation to describe the shape, similar to the ASM or AAM. To fit this shape model, we maximize the score of an appearance model defined by GentleBoost, which employs local histograms of oriented gradients. The second approach is based on the cascade regression shape scheme. The main idea is the approximation for each step into a classification of homogeneous deformations, grouped by unsupervised clustering.The third module is the re-identfication one. We show that employing a PDM as a structural support improves re-identification results. We experiment classic appearance-based signatures, color histograms and the shape descriptor Shape Context. The results are encouraging for application perspective of PDM for the gait recognition.; L'essor des systèmes mobiles pose de nouvelles problématiques dans le domaine de vision par ordinateur. Les techniques de ré-identification s'appuyant sur un réseau de caméras fixes doivent être repensées afin de s'adapter à un décor changeant. Pour répondre à ces besoins, cette thèse explore, dans le cadre du corps humain, l'utilisation d'un modèle structurel habituellement employé pour de la reconnaissance faciale. Il s'agit de l'alignement d'un modèle à distribution de points (Point Distribution Model ou PDM). L'objectif de ce pré-traitement avant la ré-identification est triple, segmenter la personne du décor, améliorer la robustesse vis-à-vis de sa pose et extraire des points clés spatiaux pour construire une signature basée sur son comportement.Nous concevons et évaluons un système complet de ré-identification, découpé en trois modules mis en séquence. Le premier de ces modules correspond à la détection de personnes. Nous proposons de nous baser sur une méthode de l'état de l'art utilisant les Channel Features avec l'algorithme AdaBoost.Le second module est l'alignement du PDM au sein de la boîte englobante fournie par la détection. Deux approches sont présentées dans cette thèse. La première s'appuie sur une formulation paramétrique du modèle de forme. L'alignement de ce modèle est guidé par la maximisation d'un score d'un modèle d'apparence GentleBoost utilisant des caractéristiques locales de type histogrammes de gradients orientés. La seconde approche exploite une technique de cascade de régressions de forme. L'idée principale est le regroupement de déformations homogènes en clusters et la classification de ces derniers dans le but d'aligner le PDM itérativement.Enfin, le troisième module est celui de la ré-identification. Nous montrons que l'utilisation d'un PDM en support permet d'améliorer les résultats de ré-identification. Nos expérimentations portent sur des signatures d'apparence classique, les histogrammes de couleurs, et sur un descripteur de forme, le Shape Context. L'évaluation de ce dernier fournit des résultats encourageants pour une perspective d'utilisation des PDM au sein d'une reconnaissance de démarches.
- Published
- 2016
41. Facial Animation Based on 2D Shape Regression
- Author
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Qiqi Hou, Ruibin Bai, Yihong Gong, and Jinjun Wang
- Subjects
Facial expression ,Head (linguistics) ,business.industry ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Shape regression ,020207 software engineering ,02 engineering and technology ,Animation ,Convolutional neural network ,stomatognathic diseases ,Video tracking ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business ,Computer facial animation ,ComputingMethodologies_COMPUTERGRAPHICS ,Avatar - Abstract
We present a facial animation system for ordinary single-cameral videos based on 2D shape regression. Unlike some prior facial animation techniques, our system doesn’t need complex equipment. The system consists of firstly a Cascade Multi-Channel Convolutional Neural Network (CMC-CNN) model to accurately detect facial landmarks from 2D video frames. Based on these detected 2D points, the facial motion parameters, including the head pose and facial expressions, are recovered. Then the system animates a bone-driven 3D avatar with the facial motion parameters. Experiments show that our system can accurately detect facial landmarks and the animation results are visually plausible and similar to the user’s facial motion.
- Published
- 2016
42. Spatiotemporal modeling of anatomical shape complexes
- Author
-
Fishbaugh, James
- Subjects
Spatiotemporal ,Shape regression ,Shape analysis - Abstract
Statistical analysis of time dependent imaging data is crucial for understanding normal anatomical development as well as disease progression. The most promising studies are of longitudinal design, where repeated observations are obtained from the same subjects. Analysis in this case is challenging due to the difficulty in modeling longitudinal changes, such as growth, and comparing changes across different populations. In any case, the study of anatomical change over time has the potential to further our understanding of many dynamic processes. What is needed are accurate computational models to capture, describe, and quantify anatomical change over time. Anatomical shape is encoded in a variety of representations, such as medical imaging data and derived geometric information extracted as points, curves, and/or surfaces. By considering various shape representations embedded into the same ambient space as a shape complex, either in 2D or 3D, we obtain a more comprehensive description of the anatomy than provided by an single isolated shape. In this dissertation, we develop spatiotemporal models of anatomical change designed to leverage multiple shape representations simultaneously. Rather than study directly the geometric changes to a shape itself, we instead consider how the ambient space deforms, which allows all embedded shapes to be included simultaneously in model estimation. Around this idea, we develop two complementary spatiotemporal models: a flexible nonparametric model designed to capture complex anatomical trajectories, and a generative model designed as a compact statistical representation of anatomical change. We present several ways spatiotemporal models can support the statistical analysis of scalar measurements, such as volume, extracted from shape. Finally, we cover the statistical analysis of higher dimensional shape features to take better advantage of the rich morphometric information provided by shape, as well as the trajectory of change captured by spatiotemporal models.
- Published
- 2016
- Full Text
- View/download PDF
43. Extended Robust Cascaded Pose Regression for Face Alignment
- Author
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Xinyu Ren, Yongxin Ge, Cheng Peng, and Xuchu Wang
- Subjects
Relation (database) ,Computer science ,business.industry ,Shape regression ,020207 software engineering ,Pattern recognition ,Feature selection ,02 engineering and technology ,Regression ,Correlation ,Face (geometry) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Artificial intelligence ,business - Abstract
We present a highly accurate and very efficient approach for face alignment, called Extended Robust Cascaded Pose Regression (ERCPR), which is robust to large variations due to differences in expressions and pose. Unlike previous shape regression-based approaches, we propose to reference features weighted by three different face landmarks, which are much more robust to shape variations. Then, a correlation-based feature selection method and a two-level boosted regression are applied to establish accurate relation between features and shapes. Experiments on two challenging face datasets (LFPW, COFW) show that our proposed approach significantly outperforms the state-of-art in terms of both efficiency and accuracy.
- Published
- 2016
44. The Interplay of Performing Level and Conformation—A Characterization Study of the Lipizzan Riding Stallions From the Spanish Riding School in Vienna
- Author
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Thomas Druml, Gottfried Brem, and Maximilian Dobretsberger
- Subjects
Competition level ,Engineering ,Body shape ,040301 veterinary sciences ,Equine ,business.industry ,0402 animal and dairy science ,Shape regression ,Mean age ,04 agricultural and veterinary sciences ,040201 dairy & animal science ,0403 veterinary science ,Physical performance ,business ,Simulation ,Demography - Abstract
Classical dressage and the schools above the ground as performed in the Spanish Riding School (SRS) in Vienna, require special psychological and physical properties from riding horses. To document the training and performing level of the Lipizzan riding stallions from the SRS in Vienna, we analyzed the horses' performance traits retrieved from chief riders' evaluations in relation to training levels and age classes and we studied the interplay of performing status with the horses' body shape. In total, the mean age of all 80 riding stallions was 11.9 years (min 4 years, max 26 years). Completely trained stallions (competition level S and higher) were on average 15.6 years old (min. 10 years and max. 26 years). From 10 recorded performance traits (five physical traits and five psychological traits), walk, trot, and collection ratings showed significant differences for levadeurs, caprioleurs, and courbetteurs; the psychological traits reactability, diligence, and sensibility showed significant differences between age class (3–4 years, 5–8 years, 9–16 years, >16 years) and number of flying gallop changes. Further we found that 80% of the chief riders' ratings of physical performance traits reached significant levels in the shape regressions, indicating an association of their ratings with body shape variation. The resulting mean body shapes from the significant regressions illustrated the requirements of the school above the ground and the classical dressage on the horses' conformation. We showed that the evaluation of subjective ratings on valuating scales applying shape regressions can help to optimize the quality of scoring data in equine performance traits.
- Published
- 2018
45. The use of novel phenotyping methods for validation of equine conformation scoring results
- Author
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Gottfried Brem, Thomas Druml, and Maximilian Dobretsberger
- Subjects
Scale (ratio) ,Image processing ,Breeding ,Bioinformatics ,SF1-1100 ,Correlation ,rater reliability ,image analysis ,Statistics ,Animals ,Horses ,Animal Husbandry ,geometric morphometrics ,Mathematics ,linear type trait ,Shape regression ,Generalized Procrustes analysis ,horse conformation ,Regression analysis ,Animal culture ,Shape space ,Phenotype ,Austria ,Trait ,Regression Analysis ,Animal Science and Zoology ,Female - Abstract
In this experiment, which is based on a cohort of 44 Lipizzan mares from the Austrian state stud farm of Piber, we present new statistical techniques for the analysis of shape and equine conformation using image data. In addition, we examined which strategies and procedures of image processing techniques led to a successful interpretation of the traits implemented in horse breeding programs. A total of 246 two-dimensional anatomical and somatometric landmarks were digitized from standardized photographs, and the variation of shape has been analyzed by the use of generalized orthogonal least-squares Procrustes (generalized Procrustes analysis (GPA)) procedures. The resulting shape variables have been regressed on the results from linear type trait classifications. In addition, the rating scores of six conformation classifiers were tested for agreement, yielding an inter-rater correlation (inter-class correlation) ranging from 0.41 to 0.68, respectively, a κ coefficient ranging from 0.16 to 0.53. From the 12 linear type traits assessed on a valuating scale, only the type-related traits (type, breed-type and harmony) revealed significant (P
- Published
- 2015
46. Face Alignment with Two-Layer Shape Regression
- Author
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Qilong Zhang and Lei Zhang
- Subjects
business.industry ,Property (programming) ,Two layer ,Shape regression ,Pattern recognition ,Machine learning ,computer.software_genre ,Regression ,Term (time) ,Categorization ,Face (geometry) ,Key (cryptography) ,Artificial intelligence ,business ,computer ,Mathematics - Abstract
We present a novel approach to resolve the problem of face alignment with a two-layer shape regression framework. Traditional regression-based methods [4, 6, 7] regress all landmarks in a single shape without consideration of the difference between various landmarks in biologic property and texture, which would lead to a suboptimal prediction. Unlike previous regression-based approach, we do not regress the entire landmarks in a holistic manner without any discrimination. We categorize the geometric constraints into two types, inter-component constraints and intra-component constraints. Corresponding to these two shape constraints, we design a two-layer shape regression framework which can be integrated with regression-based methods. We define a term of “key points” of components to describe inter-component constraints and then determine the sub-shapes. We verify our two-layer shape regression framework on two widely used datasets (LFPW [10] and Helen [11]) for face alignment and experimental results prove its improvements in accuracy.
- Published
- 2015
47. Intensity-Depth Face Alignment Using Cascade Shape Regression
- Author
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Bao-Liang Lu and Yang Cao
- Subjects
Channel (digital image) ,business.industry ,Cascade ,Computer science ,Face (geometry) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Shape regression ,Computer vision ,Feature selection ,Artificial intelligence ,business ,Intensity (heat transfer) ,Image (mathematics) - Abstract
With quick development of Kinect, depth image has become an important channel for assisting the color/infrared image in diverse computer vision tasks. Kinect can provide depth image as well as color and infrared images, which are suitable for multi-model vision tasks. This paper presents a framework for intensity-depth face alignment based on cascade shape regression. Information from intensity and depth images is combined during feature selection in cascade shape regression. Experimental results show that this combination improves face alignment accuracy notably.
- Published
- 2015
48. Combining shape regression model and isophotes curvature information for eye center localization
- Author
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Dihu Chen, Zhiyong Pang, and Chuansheng Wei
- Subjects
Computer science ,business.industry ,Robustness (computer science) ,Shape regression ,Isophote ,Pattern recognition ,Computer vision ,Artificial intelligence ,business ,Curvature ,Face shape - Abstract
The localization of eye centers technique is used in several applications. In this paper, we combine the face shape model to eye center location method to obtaining robustness in eye center localization from a low-resolution image. We make use of shape regression approach and combine it to the eye-center localization using isophote curvature features which has shown its advantage from previous work. In the experiments, we use BioID datasets to test our approach for accurate eye center location and robustness to changes in illumination and pose. We demonstrate that our approach can achieve an improvement of the robustness of eye center localization while the accuracy at eye center localization e≤0.1 and e≤0.15 have achieve 0.918 and 0.977 respectively.
- Published
- 2014
49. Extending explicit shape regression with mixed feature channels and pose priors
- Author
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Hazim Kemal Ekenel, Matthias Richter, and Hua Gao
- Subjects
Computer science ,business.industry ,feature extraction ,Feature extraction ,Entertainment industry ,shape regression ,Pattern recognition ,Image processing ,pose estimation ,facial feature detection ,Range (mathematics) ,Feature (computer vision) ,Face (geometry) ,Computer vision ,Artificial intelligence ,business ,Pose ,Feature detection (computer vision) - Abstract
Facial feature detection offers a wide range of applications, e.g. in facial image processing, human computer interaction, consumer electronics, and the entertainment industry. These applications impose two antagonistic key requirements: high processing speed and high detection accuracy. We address both by expanding upon the recently proposed explicit shape regression [1] to (a) allow usage and mixture of different feature channels, and (b) include head pose information to improve detection performance in non-cooperative environments. Using the publicly available “wild” datasets LFW [10] and AFLW [11], we show that using these extensions outperforms the baseline (up to 10% gain in accuracy at 8% IOD) as well as other state-of-the-art methods.
- Published
- 2014
50. The Interplay of Performing Level and Conformation—A Characterization Study of the Lipizzan Riding Stallions From the Spanish Riding School in Vienna.
- Author
-
Druml, Thomas, Dobretsberger, Maximilian, and Brem, Gottfried
- Abstract
Classical dressage and the schools above the ground as performed in the Spanish Riding School (SRS) in Vienna, require special psychological and physical properties from riding horses. To document the training and performing level of the Lipizzan riding stallions from the SRS in Vienna, we analyzed the horses' performance traits retrieved from chief riders' evaluations in relation to training levels and age classes and we studied the interplay of performing status with the horses' body shape. In total, the mean age of all 80 riding stallions was 11.9 years (min 4 years, max 26 years). Completely trained stallions (competition level S and higher) were on average 15.6 years old (min. 10 years and max. 26 years). From 10 recorded performance traits (five physical traits and five psychological traits), walk, trot, and collection ratings showed significant differences for levadeurs, caprioleurs, and courbetteurs; the psychological traits reactability, diligence, and sensibility showed significant differences between age class (3–4 years, 5–8 years, 9–16 years, >16 years) and number of flying gallop changes. Further we found that 80% of the chief riders' ratings of physical performance traits reached significant levels in the shape regressions, indicating an association of their ratings with body shape variation. The resulting mean body shapes from the significant regressions illustrated the requirements of the school above the ground and the classical dressage on the horses' conformation. We showed that the evaluation of subjective ratings on valuating scales applying shape regressions can help to optimize the quality of scoring data in equine performance traits. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
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