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Subjective similarity holds a prominent place in many psychological theories, influencing diverse cognitive processes ranging from attention and categorization to memory and problem solving. Despite the known importance of subjective similarity, there are few resources available to experimenters interested in manipulating the visual similarity of shape, one common type of subjective similarity. Here, across seven validation iterations, we incrementally developed a stimulus space consisting of 360 shapes using a novel image-processing method in conjunction with collected similarity judgments. The result is the Validated Circular Shape space, the first Validated Circular Shape space comparable to the commonly used "color wheel", whereby angular distance along a 2D circle is a proxy for visual similarity. This extensively validated resource is freely available to experimenters wishing to precisely manipulate the visual similarity of shape. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Knowledge of human ethnicity constitutes important biometric information. An automated ethnicity classification is a good first step in facial analysis. However, most ethnicity classification methods require a complex feature extraction and model training process. We propose a novel ethnicity classification method based on the analysis of facial landmarks in Kendall shape space. Facial features with different relative positions have a close relationship with ethnicity. Facial landmarks can represent positions of facial features. We build a Discrete Landmarks Model (DLM) based on facial landmarks and construct an ethnicity classification model based on the DLM analysis. The clear advantages of our method are that it is fully automated; requires no complex data preprocessing, feature extraction or a complex training process; results in a fast and accurate classification process. We estimate the effectiveness of our method experimentally, using public databases such as Texas3D, FRGC2.0, BU-3DFE and BU-4DFE. On average, our method can achieve a 95% ethnicity classification rate with each classification attempt in 2.0 s.

Misalignment of teeth or jaws can impact the ability to chew or speak, increase the risk of gum disease or tooth decay, and potentially influence a person’s (psychological) well-being. Orthodontic treatments of misaligned teeth are complex procedures that employ dental braces to apply forces in order to move the teeth or jaws to their correct position. Photographs are typically used to document the treatment. An automatic analysis of those photographs could support the decision making and monitoring process. In this paper, we propose an automatic model-based end-to-end 3-D reconstruction approach of the teeth from five photographs with predefined viewing directions (i.e. the photographs used in orthodontic treatment documentation). It uses photo- or view-specific 2-D coupled shape models to extract the teeth contours from the images. The shape reconstruction is then carried out by a deformation-based reconstruction approach that utilizes 3-D coupled shape models and minimizes a silhouette-based loss. The optimal model parameters are determined by an optimization which maximizes the overlaps between the projected 2-D outlines of individual teeth of the 3-D model and the contours extracted from the corresponding photograph. After that the point displacements between the projected outline and segmented contour are used to iteratively deform the 3-D shape model of each tooth for all five views. Back-projection into shape space ensures that the 3-D coupled shape model consists of (statistically) valid teeth. Evaluation on 22 data sets shows promising results with an average symmetric surface distance of 0.848mm and an average DICE coefficient of 0.659.

Cancer develops resistance to treatments through many mechanisms. Single-cell analyses reveal the intratumor heterogeneity and dynamic relationships between cancer cell subpopulations. These analyses also highlight that various mechanisms of resistance may coexist in a given tumor. Studies have unraveled how the microenvironment affects tumor response to treatments and how cancer cells may adapt to these treatments. Though challenging, individualized treatment based on the molecular characterization of the tumor should become the new standard of care. In the meantime, the success rate of clinical trials in oncology remains dramatically low. There is a need to do better and improve the predictability of preclinical models. This requires innovative changes in ex vivo models and the culture system currently being used. An innovative ligand design is also urgently needed. The limited arsenal of medicinal chemistry reactions and the biases of scaffold selection favor structurally similar compounds with linear shapes at the expense of disc and spherical shapes, which leave a large chemical shape space untouched. In this regard, venoms have received increasing interest as a wellspring for drug candidates. Overall, the characterization of tumor heterogeneity has contributed to advancing our understanding of the mechanisms that underlie cancer resistance to treatments. Targeting these mechanisms will require setting key milestones to significantly improve the translatability of preclinical studies to the clinic with the hope of increasing the success rate of clinical trials.

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments., Comment: 8 pages, 7 figures, 3 tables. Accepted to DiffCVML

Four decades of studies in visual attention and visual working memory used visual features such as colors, orientations, and shapes. The layout of their featural space is clearly established for most features (e.g., CIE-Lab for colors) but not shapes. Here, I attempted to reveal the basic dimensions of preattentive shape features by studying how shapes can be positioned relative to one another in a way that matches their perceived similarities. Specifically, 14 shapes were optimized as n-dimensional vectors to achieve the highest linear correlation (r) between the log-distances between C (14, 2) = 91 pairs of shapes and the discriminabilities (d') of these 91 pairs in a texture segregation task. These d' values were measured on a large sample (N = 200) and achieved high reliability (Cronbach's α = 0.982). A vast majority of variances in the results (r = 0.974) can be explained by a three-dimensional SCI shape space: segmentability, compactness, and spikiness.

Learning to reconstruct 3D shapes using 2D images is an active research topic, with benefits of not requiring expensive 3D data. However, most work in this direction requires multi-view images for each object instance as training supervision, which oftentimes does not apply in practice. In this paper, we relax the common multi-view assumption and explore a more challenging yet more realistic setup of learning 3D shape from only single-view images. The major difficulty lies in insufficient constraints that can be provided by single view images, which leads to the problem of pose entanglement in learned shape space. As a result, reconstructed shapes vary along input pose and have poor accuracy. We address this problem by taking a novel domain adaptation perspective, and propose an effective adversarial domain confusion method to learn pose-disentangled compact shape space. Experiments on single-view reconstruction show effectiveness in solving pose entanglement, and the proposed method achieves on-par reconstruction accuracy with state-of-the-art with higher efficiency., Comment: under review, code available at https://github.com/bomb2peng/learn3D

Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

We aimed to develop the first fully automated 3D gallbladder segmentation approach to perform volumetric analysis in volume data of magnetic resonance (MR) cholangiopancreatography (MRCP) sequences. Volumetric gallbladder analysis is performed for non-contrast-enhanced and secretin-enhanced MRCP sequences. Native and secretin-enhanced MRCP volume data were produced with a 1.5-T MR system. Images of coronal maximum intensity projections (MIP) are used to automatically compute 2D characteristic shape features of the gallbladder in the MIP images. A gallbladder shape space is generated to derive 3D gallbladder shape features, which are then combined with 2D gallbladder shape features in a support vector machine approach to detect gallbladder regions in MRCP volume data. A region-based level set approach is used for fine segmentation. Volumetric analysis is performed for both sequences to calculate gallbladder volume differences between both sequences. The approach presented achieves segmentation results with mean Dice coefficients of 0.917 in non-contrast-enhanced sequences and 0.904 in secretin-enhanced sequences. This is the first approach developed to detect and segment gallbladders in MR-based volume data automatically in both sequences. It can be used to perform gallbladder volume determination in epidemiological studies and to detect abnormal gallbladder volumes or shapes. The positive volume differences between both sequences may indicate the quantity of the pancreatobiliary reflux.

Existing methods for reconstructing 3D faces from multiple unconstrained images mainly focus on generating a canonical identity shape. This paper instead aims to optimize both the identity shape and the deformed shapes unique to individual images. To this end, we disentangle 3D face shapes into identity and residual components and leverage facial landmarks on the 2D images to regress both component shapes in shape space directly. Compared with existing methods, our method reconstructs more personal-ized and visually appealing 3D face shapes thanks to its ability to effectively explore both common and different shape characteristics among the multiple images and to cope with various shape deformation that is not limited to expression changes. Quantitative evaluation shows that our method achieves lower reconstruction errors than state-of-the-art methods.

Although we can all agree that interference induces forgetting, there is surprisingly little consensus regarding what type of interference most likely disrupts memory. We previously proposed that the similarity of interference differentially impacts the representational detail of color memory. Here, we extend this work by applying the Validated Circular Shape Space (Li et al., 2020) for the first time to a continuous retrieval task, in which we quantified both the visual similarity of distracting information as well as the representational detail of shape memory. We found that the representational detail of memory was systematically and differentially altered by the similarity of distracting information. Dissimilar distractors disrupted both fine- and coarse-grained information about the target, akin to memory erasure. In contrast, similar distractors disrupted fine-grained target information but increased reliance on coarse-grained information about the target, akin to memory blurring. Notably, these effects were consistent across two mixture models that each implemented a different scaling metric (either angular distance or perceived target similarity), as well as a parameter-free analysis that did not fit the mixture model. These findings suggest that similar distractors will help memory in cases where coarse-grained information is sufficient to identify the target. In other cases where precise fine-grained information is needed to identify the target, similar distractors will impair memory. As these effects have now been observed across both stimulus domains of shape and color, and were robust across multiple scaling metrics and methods of analyses, we suggest that these results provide a general set of principles governing how the nature of interference impacts forgetting.

OBJECTIVES: To assess the potential of predicting adult facial types at different stages of mandibular development. SETTING AND SAMPLE POPULATION: 941 participants from the Bolton-Brush, Denver, Fels, Iowa, Michigan, and Oregon growth studies with longitudinal lateral cephalograms (total of 7,166) between ages 6–21 years. MATERIAL & METHODS: Each participant was placed into one of three facial types based on mandibular plane angle (MPA) from cephalograms taken closest to 18 years of age (range of 15–21 years): hypo-divergent (MPA39°). Cephalograms were categorized into 13 age groups 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18–21. Twenty-three two-dimensional anatomical landmarks were digitized on the mandible and superimposed using generalized Procrustes analysis, which projects landmarks into a common shape space. Data were analyzed within age categories using stepwise discriminant analysis to identify landmarks that distinguish adult facial types and by jackknife cross-validation to test how well young individuals can be re-classified into their adult facial types. RESULTS: Although each category has multiple best discriminating landmarks among adult types, three landmarks were common across nearly all age categories: menton, gonion, and articulare. Individuals were correctly classified better than chance, even among the youngest age category. Cross-validation rates improved with age, and hyper- and hypo-divergent groups have better reclassification rates than the normo-divergent group. CONCLUSIONS: The discovery of important indicators of adult facial type in the developing mandible helps improve our capacity to predict adult facial types at a younger age.

Focusing on geometric morphometrics (GMM), we review methods for acquiring morphometric data from 3-D objects (including fossils), algorithms for producing shape variables and morphospaces, the mathematical properties of shape space, especially how they relate to morphogenetic and evolutionary factors, and issues posed by working with fossil objects. We use the Raupian shell-coiling equations to illustrate the complexity of the relationship between such factors and GMM morphospaces. The complexity of these issues re-emphasize what are arguably the two most important recommendations for GMM studies: 1) always use multivariate methods and all of the morphospace axes in an analysis; and 2) always anticipate the possibility that the factors of interest can have complex, nonlinear relationships with shape.

Being a class of effective feature descriptors for action recognition, action representations based on skeleton sequences have yielded excellent recognition results. Most methods used to construct these action representations are based on the information from all the joint positions in actions. Unfortunately, some joints in the actions do not improve the accuracy of action recognition, and may even cause unnecessary inter-class errors. In this study, the authors propose a new method for action recognition by selecting active joints which are closely related to the movement of the body as the first step. Further, a skeleton is characterized as a set of its active-joint positions, and the set can be mapped to a point on pre-shape space to filter out the scale and translation variability. Then, a skeleton sequence (an action) can be regarded as points on the space. Because the timing-sequence relationship between skeletons is very valuable for action recognition, a tensor-based linear dynamical system (tLDS) is employed to model the temporal information of the action. To avoid using a finite-order sequence to estimate the infinite-order feature descriptor of a tLDS, the descriptor is mapped to a point on an infinite Grassmannian composed of the extended observability subspaces. The action is classified using sparse coding and dictionary learning (SCDL) on the infinite Grassmannian. Experimental results demonstrate that the recognition accuracies of the proposed method outperform state-of-the-art ones on four different action datasets.

Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realisticly dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input without requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics. Code and data are available at our project webpage.

The research field entitled structural DNA nanotechnology emerged in the beginning of the 1980s as the first immobile synthetic nucleic acid junctions were postulated and demonstrated. Since then, the field has taken huge leaps toward advanced applications, especially during the past decade. This Progress Report summarizes how the controllable, custom, and accurate nanostructures have recently evolved together with powerful design and simulation software. Simultaneously they have provided a significant expansion of the shape space of the nanostructures. Today, researchers can select the most suitable fabrication methods, and design paradigms and software from a variety of options when creating unique DNA nanoobjects and shapes for a plethora of implementations in materials science, optics, plasmonics, molecular patterning, and nanomedicine.

A statistical framework was developed for parametric modeling of human body shapes.We developed a non-linear optimization-based optimal body shape modeling technique.New body shapes were generated by inputting linear anthropometric parameters.Resultant models were segmented into 16 key regions of the human body.Resultant models contained information on 15 key skeleton joints. This paper proposes a new example-based statistical modeling framework to conduct the parametric modeling of 3D human body shapes from linear anthropometric parameters. The modeling framework consists of the following three phases: construction of a training database of human body shapes, statistical analysis of human body shapes and human body shape modeling. In the training database construction phase, a consistent parameterization was carried out on 3D whole-body scan data of 80 males and 80 females with a wide variety of body shapes as the examples for this study. The surface-fitting process, which was improved relative to existing methods, was used to guarantee the high-quality parameterization in this phase and to generate an articulated body shape model in the modeling phase. To characterize the range of body shape variation, the training database was analyzed statistically. Additionally, a correlation between the body shapes and the anthropometric parameters was learned in the statistical analysis phase to estimate body shapes from intuitive and semantic parameters. A new technique to generate an optimal body shape model that precisely satisfies user input body dimensions was developed in the model generation phase. This technique enables the estimation of body shape variation, not only within the body shape space that was learned statistically, but also outside of the body shape space, while maintaining body shapes that stay in the human shape space. Our approach produced reasonable results having a high modeling accuracy satisfying user-specified anthropometric parameters, and high visual quality in expressing realistic body shapes. The resultant models were then segmented into 16 key regions of the human body, and had information on 15 key joints, and thus they could be a useful tool in various industries. Compared with other statistical modeling approaches, the proposed method contributes to related areas by introducing an improved surface-fitting process and a non-linear optimization-based optimal body shape modeling technique.

This paper considers the roles of policing and security in the geographies of everyday public and semi-public space. We contend that while security is concerned with territory, policing relates to place. We consider the relationship between security and territory before examining the relationship between policing and place. In the final section, we argue that a relational view of space is needed to understand how practices of policing and security shape space and, in turn, the lives of people using it.

In this paper we study the shape space of curves with values in a homogeneous space $M = G/K$, where $G$ is a Lie group and $K$ is a compact Lie subgroup. We generalize the square root velocity framework to obtain a reparametrization invariant metric on the space of curves in $M$. By identifying curves in $M$ with their horizontal lifts in $G$, geodesics then can be computed. We can also mod out by reparametrizations and by rigid motions of $M$. In each of these quotient spaces, we can compute Karcher means, geodesics, and perform principal component analysis. We present numerical examples including the analysis of a set of hurricane paths., Comment: To appear in 3rd International Workshop on Diff-CVML Workshop, CVPR 2017

Modelling computer vision problems with Riemannian manifolds yields excellent results given that the visual features of these maniflods have special structures that Euclidean space doesn’t capture. In this paper we propose an approach based on the Kendall manifold formalism and the Bayesian approach applied to a plant species classification problem. Kendall space is a quotient space that is provided with a Riemannian metric, which is more convenient when shapes differ only in translation, rotation and scale. However, an appropriate metric for shape classification task should not only suit certain invariance properties but also satisfy the different input properties. The non-linearity of Kendall space makes it difficult to apply common algorithms for classification. Thus, we propose to adapt the Bayes classifier to Kendalls representation using landmarks. Our main contribution consists in computing the parameters of the likelihood density functions through tangent spaces. Experimental results show that our approach is more accurate and effective.

Face alignment is important for most facial analysis system. Regression based methods directly map the input face to shape space, make them sensitive to the face bounding boxes. In this work, we aim at developing a model that can deal with complex non-linear variations and be invariant to face bounding box distributions, while preserving high alignment accuracy. We define response map for each facial point, which is a 2D probability map indicating the presence likelihood of facial point at the corresponding locations. We solve the face alignment problem by two-stage processes. The first stage is response mapping stage, we use deep Purely Convolutional Network (a specialised Convolutional Neural Network designed for face alignment problem) to reconstruct the response maps. The second stage is shape mapping stage, which processes the response maps to get locations of facial key points. We explored four functions for this stage: max function, max + PCA, mean function and mean + PCA function. Experiments done on 300 W dataset show that our algorithm outperforms state-of-the-art methods.

We present a new method for rapidly measuring child body shapes from noisy, incomplete data captured from low-cost depth cameras. This method fits the data using a statistical body shape model (SBSM) to find a complete avatar in the realistic body shape space. The method also predicts a set of standard anthropometric data for a specific subject without measuring dimensions directly from the fitted model. Since the SBSM was developed using principal component (PC) analysis, we formulate an optimisation problem to fit the model in which the degrees of freedom are defined in PC-score space. The mean unsigned distance between the fitted-model based on depth-camera data and the high-resolution laser scan data was 9.4 mm with a standard deviation (SD) of 5.1 mm. For the torso, the mean distance was 2.9 mm (SD 1.4 mm). The correlations between standard anthropometric dimensions predicted by the SBSM and manually measured dimensions exceeded 0.9.

The facial performance of an individual is inherently rich in subtle deformation and timing details. Although these subtleties make the performance realistic and compelling, they often elude both motion capture and hand animation. We present a technique for adding fine-scale details and expressiveness to low-resolution art-directed facial performances, such as those created manually using a rig, via marker-based capture, by fitting a morphable model to a video, or through Kinect reconstruction using recent faceshift technology. We employ a high-resolution facial performance capture system to acquire a representative performance of an individual in which he or she explores the full range of facial expressiveness. From the captured data, our system extracts an expressiveness model that encodes subtle spatial and temporal deformation details specific to that particular individual. Once this model has been built, these details can be transferred to low-resolution art-directed performances. We demonstrate results on various forms of input; after our enhancement, the resulting animations exhibit the same nuances and fine spatial details as the captured performance, with optional temporal enhancement to match the dynamics of the actor. Finally, we show that our technique outperforms the current state-of-the-art in example-based facial animation.

This paper presents technological and conceptual visions beyond the traditional planning horizon of space agencies. It relies on the research and reflections within the larger advanced concepts research community created by and around the European Space Agency's Advanced Concepts Team as well as the results of a two-day long symposium in July 2012, including Europe's first space ‘un-conference’, focussed on re-thinking the future of space beyond the traditional thought boundaries of the space sector. For this purpose it reviews visions and expectations formulated at the creation of the ACT, results obtained and fundamental changes that are expected to shape space activities and the space sector in a 10–15+ years time frame, while relaying these to specific ongoing research activities.

Object recognition is one of the most important tasks in computer vision domain and it has been extensively applied in many areas during the last three decades. In this paper, a method is proposed to recognize objects in shape spaces in which landmarks have three kinds of information, i.e., landmarks position, angle and feature vector. By comparing angle and feature vector, a landmark in an image can correspond with a few number of landmarks in another image. Then, the pixel's positions of landmarks from two images are used to calculate the Procrustean distance in a shape space. If the value of the Procrustean distance is smaller than a predefined threshold, the two sets of landmarks will be considered to match each other. Applying the proposed method, two-dimensional objects including occluded objects can be recognized quickly.

In this paper we present an evaluation of a robust visual image tracker on echocardiographic image sequences. We show how the tracking framework can be customized to define an appropriate shape space that describes heart shape deformations that can be learnt from a training data set. We also investigate energy-based temporal boundary enhancement methods to improve image feature measurement. Results are presented demonstrating real-time tracking on real normal heart motion data sequences and abnormal synthesized and real heart motion data sequences. We conclude by discussing some of our current research efforts.

This contribution demonstrates an example of experimental automatic image analysis to detect spores prepared on microscope slides derived from trapping. The application is to monitor aerial spore counts of the entomopathogenic fungus Pandora neoaphidis which may serve as a biological control agent for aphids. Automatic detection of such spores can therefore play a role in plant protection. The present approach for such detection is a modification of traditional manual microscopy of prepared slides, where autonomous image recording precedes computerised image analysis. The purpose of the present image analysis is to support human visual inspection of imagery data – not to replace it. The workflow has three components: • Preparation of slides for microscopy. • Image recording. • Computerised image processing where the initial part is, as usual, segmentation depending on the actual data product. Then comes identification of blobs, calculation of principal axes of blobs, symmetry operations and projection on a three parameter egg shape space.

The incorporation of a multiresolution image approach is one of the most popular variants of Active Shape Models (ASMs), providing a more robust algorithm and minimizing its initialization dependency. Using the wavelet transform, the present paper extends the multiresolution analysis to the shape space, developing a novel multiresolution shape framework, capable of being incorporated into most of ASM variants. The tests performed with two different types of images, face images (AR database) and chest radiographs (JSRT database), demonstrate how this new generation of algorithms significantly reduce the computational cost, more than halving it, while maintaining the same levels of accuracy.

In this paper, we propose a method for generating a realistic object panorama by considering geometric transformations of camera views in a general photographing environment. In the proposed method, we first model a trumpet-shape panorama space based on geometric transformations of camera, such as vertical rotation and horizontal rotation movement around a target model. We then generate an object panorama by mapping model images to the trumpet-shape panorama space. To evaluate the performance of the proposed method, experiments were conducted on a large size model, which is quite difficult for us to generate the object panorama without special equipments in general. The experimental results show that the proposed method can effectively generate an object panorama, which is usually generated in a special photographing environment, regardless of the size of target model.

Here we use the state-of-the-art invariant imbedding T-matrix method to theoretically assess the backscattering linear depolarization ratio (LDR) of nonspherical particles in a super-ellipsoidal shape space. Super-ellipsoids have inherent flexibility to model the particle aspect ratio, roundness, and concavity, these being salient characteristics of most atmospheric particles (e.g., sea salt and dust aerosols). The complex refractive index of super-ellipsoids was set up with the real part ranging from 1.1 to 2.0 and the imaginary part from 10−7 to 0.5. To constrain the computational burden, the maximum size parameters for spheroids and super-ellipsoids were set as 100 and 50, respectively. From the LDRs of spheroids, we found that enhanced LDRs (>~60%) are common for optically soft particles. However, as the real part of the refractive index increases (larger than ~1.33), the enhanced LDRs (>~60%) are in high probability observed for nearly-spherical particles, and then disappear as the refractive index exceeds 1.7. To produce the enhanced LDRs, the imaginary part of the refractive index should also be less than ~0.01 such that the backscattered waves from particle-to-air transmission have sizable contributions, as the external reflection of spheroids produces no depolarization. This finding has particular relevance to LiDAR observations of atmospheric particles because the refractive index of most aerosols and hydrometeors at the LiDAR wavelength (e.g., 0.532μm) locates in this region, and aerosols and hydrometeors could have nearly-spherical morphologies. From the LDRs for general super-ellipsoids, we found that the enhanced LDRs (>~60%) exist for nearly-spherical particles with the aspect ratio close to unity, but disappear for super-ellipsoids with an aspect ratio at unity. In addition, the LDRs trend to decrease as the real part of the refractive index increases for convex super-ellipsoids, but show different features for concave super-ellipsoids. Furthermore, super-ellipsoids with different roundness parameters have a distinct dependence on the aspect ratio, which is significantly different from spheroids. The results presented here provide comprehensive references for understanding the LDR change of atmospheric aerosols as the particle shape and refractive index for interpreting LiDAR backscattering signals.

Much of contemporary research in Artificial Immune Systems (AIS) has partitioned into either algorithmic machine learning and optimisation, or, modelling biologically plausible dynamical systems, with little overlap between. We propose that this dichotomy is somewhat to blame for the lack of significant advancement of the field in either direction and demonstrate how a simplistic interpretation of Perelson’s shape-space formalism may have largely contributed to this dichotomy. In this paper, we motivate and derive an alternative representational abstraction. To do so we consider the validity of shape-space from both the biological and machine learning perspectives. We then take steps towards formally integrating these perspectives into a coherent computational model of notions such as life-long learning, degeneracy, constructive representations and contextual recognition—rhetoric that has long inspired work in AIS, while remaining largely devoid of operational definition.

By analyzing interpoint comparisons, we obtain significant results describing the relationship in “hippocampus shape space” of clinically depressed, high-risk, and control populations. In particular, our analysis demonstrates that the high-risk population is closer in shape space to the control population than to the clinically depressed population.

Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance. This paper investigates thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of the number of 3D vertices; (iii) the way of using standalone automated landmark detection methods; and (iv) the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised, which can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. Moreover, an effective training method is proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been done with comparison to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of proposed method., 11 pages, 11 figures

We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.

This paper describes a novel framework for computing geodesic paths in shape spaces of spherical surfaces under an elastic Riemannian metric. The novelty lies in defining this Riemannian metric directly on the quotient (shape) space, rather than inheriting it from pre-shape space, and using it to formulate a path energy that measures only the normal components of velocities along the path. In other words, this paper defines and solves for geodesics directly on the shape space and avoids complications resulting from the quotient operation. This comprehensive framework is invariant to arbitrary parameterizations of surfaces along paths, a phenomenon termed as gauge invariance. Additionally, this paper makes a link between different elastic metrics used in the computer science literature on one hand, and the mathematical literature on the other hand, and provides a geometrical interpretation of the terms involved. Examples using real and simulated 3D objects are provided to help illustrate the main ideas., 15 pages, 11 Figures, to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence in a better resolution

We present a new formulation of a shape space containing all continuously defined 2D spline curves up to a similarity transform of a reference shape. We are able to measure a distance between an arbitrary curve and the shape space itself. Our contribution is an explicit formula for this distance measure in the continuous domain. This allows us to define efficient snake energies based on shape-dependent prior knowledge to facilitate segmentation in bioimaging. The spline-based algorithm that we propose allows us to implement continuous-domain solutions with no additional computational cost compared to the case where curves are described by a discrete set of landmarks. The proposed implementation is freely available in the public domain.

Fragment screening is one approach to hit identification for early stage drug discovery projects. Like any screening library, diversity is needed in fragment libraries. This includes coverage of shape and electrostatic space, as well as chemotype diversity. A new, easily interpretable shape-based fingerprint is described and its utility in probing fragment library content is demonstrated using a Rule of Three library from Maybridge. This method explicitly considers size as a component of shape. It allows interrogation of shape space on both a per conformer and a per molecule level, and includes a measure of flexibility. This allows for the identification of highly flexible compounds and their exclusion from the analysis. A comparison with two literature methods, the triangle plot approach of Sauer and Schwarz and the plane of best fit method of Firth et al., is also included.

In this expository article, we justify the use of sparse local descriptors for correspondence, and illustrate a systematic method for their design. Correspondence is the process that allows using image data to infer properties of the “scene,” where the scene can refer to a specific object or landscape, or can be abstracted into a category label to take into account intra-class variability. As the generality increases, the complexity of nuisance factors does too, so global pixel-level correspondence is not viable, and one has to settle instead for sparse descriptors. These should be co-designed with the classifier, and for a given classifier family, one can design the descriptors to be invariant to uninformative nuisances that are explicitly modeled, insensitive to other nuisances that are not explicitly modeled, and maximally discriminative, relative to the chosen family of classifiers. Existing descriptors are interpreted in this framework, where their limitations are illustrated, together with pointers on how to improve them.

Transcatheter aortic valve implantation (TAVI) is becoming a standard treatment for non-operable and high-risk patients with symptomatic severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, comprehensive preoperative planning is crucial for a successful outcome, with the most important decisions made during planning being the selection of the proper implant size, and determining the correct C-arm angulations. While geometric models extracted from 3D images are often used to derive these measurements, the complex shape variation of the AV anatomy found in these patients causes many of the shape representations used to estimate such geometric models to fail in capturing morphological characteristics in sufficient detail. In addition, most current approaches only model the aortic valve (AV), omitting modeling the left ventricle outflow tract (LVOT) entirely despite its high correlation with severe complications such as annulus ruptures, paravalvular leaks and myocardial infarction. We propose a fully automated method to extract patient specific models of the AV and the LVOT, and derive comprehensive biomarkers for accurate TAVI planning. We utilize a novel shape representation – the ShapeForest – which is able to model complex shape variation, preserves local shape information, and incorporates prior knowledge during shape space inference. Extensive quantitative and qualitative experiments performed on 630 volumetric data sets demonstrate an accuracy of 0.69 mm for the AV and 0.83 mm for the LVOT, an improvement of over 16% and 18% respectively when compared against state of the art methods.

In this paper, we propose a novel cascaded face shape space pruning algorithm for robust facial landmark detection. Through progressively excluding the incorrect candidate shapes, our algorithm can accurately and efficiently achieve the globally optimal shape configuration. Specifically, individual landmark detectors are firstly applied to eliminate wrong candidates for each landmark. Then, the candidate shape space is further pruned by jointly removing incorrect shape configurations. To achieve this purpose, a discriminative structure classifier is designed to assess the candidate shape configurations. Based on the learned discriminative structure classifier, an efficient shape space pruning strategy is proposed to quickly reject most incorrect candidate shapes while preserve the true shape. The proposed algorithm is carefully evaluated on a large set of real world face images. In addition, comparison results on the publicly available BioID and LFW face databases demonstrate that our algorithm outperforms some state-of-the-art algorithms.

This paper describes an application allowing content-based retrieval. The application may be called `sketch-based database retrieval? since the user interacts with the database by means of sketches. The user draws his request with a pencil: the request image is then a binary image that comprises a contour on a uniform bottom. The image-retrieval based on user-provided information such as hand-drawn sketches is a challenging problem in multimedia applications. A human's perception of shape can be rather subjective and has a large variance. In this paper, we propose to represent a sketch and a contours in image database by curvature scale space (CSS) description, which define an invariant distance in the shapes space. In theoretical aspect, the invariance, the completeness and the stability properties of CSS description imply the definition of a metric on the shape space. This distance allows us to compare a sketch to images in database. A prototype system has been developed for image retrieval by sketch. This system takes into account spatial relationships between objects in multi-objects queries.

Analysis of dynamic object deformations such as cardiac motion is of great importance, especially when there is a necessity to visualize and compare the deformation behavior across subjects. However, there is a lack of effective techniques for comparative visualization and assessment of a collection of motion data due to its 4-dimensional nature, i.e., timely varying three-dimensional shapes. From the geometric point of view, the motion change can be considered as a function defined on the 2D manifold of the surface. This paper presents a novel classification and visualization method based on a medial surface shape space, in which two novel shape descriptors are defined, for discriminating normal and abnormal human heart deformations as well as localizing the abnormal motion regions. In our medial surface shape space, the geodesic distance connecting two points in the space measures the similarity between their corresponding medial surfaces, which can quantify the similarity and disparity of the 3D heart motions. Furthermore, the novel descriptors can effectively localize the inconsistently deforming myopathic regions on the left ventricle. An easy visualization of heart motion sequences on the projected space allows users to distinguish the deformation differences. Our experimental results on both synthetic and real imaging data show that this method can automatically classify the healthy and myopathic subjects and accurately detect myopathic regions on the left ventricle, which outperforms other conventional cardiac diagnostic methods.

The wall thickness is known as a valuable measure for the cardiac diagnosis. From the geometric point of view, it can be considered as a function defined on the 2D manifold of the medial surface. This paper presents a novel classification method based on medial representation to diagnose and detect the myopathic regions on the left ventricle. A shape space is proposed and constructed based on the changes of the left ventricle wall thickness, in which two shape descriptors are introduced which show remarkable performance to distinguish normal and abnormal left ventricle deformations. The experimental results show that this method can automatically classify the healthy and myopathic subjects and detect myopathic regions on the left ventricle well.

The shapes of plant leaves are of great importance to plant biologists and botanists, as they can help in distinguishing plant species, measuring their health, analyzing their growth patterns, and understanding relations between various species. We propose a statistical model that uses the Squared Root Velocity Function representation and a Riemannian elastic metric to model the observed variability in the shape of plant leaves. We show that under this representation, one can compute sample means and principal modes of variations and can characterize the observed shapes using probability models, such as Gaussians, on the tangent spaces at the sample means. The approach is fully automatic and does not require precomputing correspondences between the shapes. We validate these statistical models by analyzing their classification performance on standard benchmarks and show their utility as generative models for random sampling. Refereed/Peer-reviewed

Prostate segmentation in transrectal ultrasound (TRUS) images is highly desired in many clinical applications. However, manual segmentation is difficult, time consuming and irreproducible. In this paper, we present a novel automatic approach using narrow band contrast pattern to segment prostates in TRUS images. Implicit representation of the segmenting level sets curve is firstly trained via principal component analysis, which also constraints the shape of prostate into a linear subspace. Then the model evolves to segment the prostate by maximizing the contrast in a narrow band near the segmenting curve. Many experimental results demonstrate the performance of the proposed algorithm, whose favorableness is validated by comparing to the state-of-the-art algorithms. Especially, the shape of prostate segmented by our algorithm is close to the one manually obtained by expert, and the mean absolute distance is only 1.07±0.77mm, which is quite promising.