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Your search keyword '"NON-RIGID MATCHING"' showing total 5 results
Start Over Descriptor "NON-RIGID MATCHING" Topic computer vision
5 results on '"NON-RIGID MATCHING"'

1. 有约束Patch-Match框架下的非刚体匹配算法.

Author

黄滢, 陈建胜, and 汪承义

Abstract

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Published
2018
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2. An Expression Deformation Approach to Non-rigid 3D Face Recognition.

Author

Al-Osaimi, F., Bennamoun, M., and Mian, A.

Subjects
*VECTOR spaces, *FACE perception, *EIGENVECTORS, *MATRICES (Mathematics), *PATTERN recognition systems, *FEATURE extraction, *COMPUTER vision
Abstract

The accuracy of non-rigid 3D face recognition approaches is highly influenced by their capacity to differentiate between the deformations caused by facial expressions from the distinctive geometric attributes that uniquely characterize a 3D face, interpersonal disparities. We present an automatic 3D face recognition approach which can accurately differentiate between expression deformations and interpersonal disparities and hence recognize faces under any facial expression. The patterns of expression deformations are first learnt from training data in PCA eigenvectors. These patterns are then used to morph out the expression deformations. Similarity measures are extracted by matching the morphed 3D faces. PCA is performed in such a way it models only the facial expressions leaving out the interpersonal disparities. The approach was applied on the FRGC v2.0 dataset and superior recognition performance was achieved. The verification rates at 0.001 FAR were 98.35% and 97.73% for scans under neutral and non-neutral expressions, respectively. [ABSTRACT FROM AUTHOR]

Published
2009
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3. DeepMatching: Hierarchical Deformable Dense Matching

Author

Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid, Jerome Revaud, Xerox Research Centre Europe [Meylan], Xerox Company, Apprentissage de modèles à partir de données massives (Thoth ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ERC_Allegro, MSR-Inria, AXES, ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), European Project: 320559,EC:FP7:ERC,ERC-2012-ADG_20120216,ALLEGRO(2013), European Project: 269980,EC:FP7:ICT,FP7-ICT-2009-6,AXES(2011), Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)

Subjects
FOS: Computer and information sciences, deep convolutional neural networks (CNN), Computer science, Computer Vision and Pattern Recognition (cs.CV), Computation, Computer Science - Computer Vision and Pattern Recognition, Optical flow, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 02 engineering and technology, optical flow, Artificial Intelligence, Robustness (computer science), Motion estimation, 0202 electrical engineering, electronic engineering, information engineering, Computer vision, Blossom algorithm, business.industry, Detector, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Robotics, Non-rigid matching, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Affine transformation, Artificial intelligence, business, Software
Abstract

International audience; We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al. A comparison of affine region detectors, 2005), the MPI-Sintel (Butler et al. A naturalistic open source movie for optical flow evaluation, 2012) and the Kitti (Geiger et al. Vision meets robotics: The KITTI dataset, 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures. We also apply DeepMatching to the computation of optical flow, called DeepFlow, by integrating it in the large displacement optical flow (LDOF) approach of Brox and Malik (Large displacement optical flow: descriptor matching in variational motion estimation, 2011). Additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation.

Published
2016
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4. Anisotropic Laplace-Beltrami Operators for Shape Analysis

Author

Daniel Cremers, Emanuele Rodolà, Mathieu Andreux, Mathieu Aubry, École polytechnique (X), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Models of visual object recognition and scene understanding (WILLOW), Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), imagine [Marne-la-Vallée], Centre Scientifique et Technique du Bâtiment (CSTB)-École des Ponts ParisTech (ENPC)-Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM)-Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-Université Paris-Est Marne-la-Vallée (UPEM), Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), École des Ponts ParisTech (ENPC), Computer Vision and Pattern Recognition Group [Münster] (CVPR), Département d'informatique - ENS Paris (DI-ENS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Inria Paris-Rocquencourt, Institut National de Recherche en Informatique et en Automatique (Inria), Aubry, Mathieu, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Paris-Rocquencourt, Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)-Centre Scientifique et Technique du Bâtiment (CSTB), and Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)

Subjects
anisotropic di usion, Anisotropic diffusion, Anisotropy, Computer vision, Diffusion, segmentation, shape analysis, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Mathematics::Spectral Theory, Topology, Curvature, Operator (computer programming), [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], non-rigid matching, Laplace–Beltrami operator, Heat kernel signature, Principal curvature, Laplace-Beltrami operator, curvature, Applied mathematics, Laplace operator, Shape analysis (digital geometry), Mathematics
Abstract

International audience; This paper introduces an anisotropic Laplace-Beltrami operator for shape analysis. While keeping useful properties of the standard Laplace-Beltrami operator, it introduces variability in the directions of principal curvature, giving rise to a more intuitive and semantically meaningful diffusion process. Although the benefits of anisotropic diffusion have already been noted in the area of mesh processing (e.g. surface regularization), focusing on the Laplacian itself, rather than on the diffusion process it induces, opens the possibility to effectively replace the omnipresent Laplace-Beltrami operator in many shape analysis methods. After providing a mathematical formulation and analysis of this new operator, we derive a practical implementation on discrete meshes. Further, we demonstrate the effectiveness of our new operator when employed in conjunction with different methods for shape segmentation and matching.

Published
2014

5. Automatic Retrieval of Anatomical Structures in 3D Medical Images

Author

Nicholas Ayache, Jérôme Declerck, Jean-Philippe Thirion, Gérard Subsol, Medical imaging and robotics (EPIDAURE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), INRIA, and Ayache, Nicholas

Subjects
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computer science, business.industry, Anatomical structures, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ELECTRONIC ATLAS, FEATURE LINE, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, NON-RIGID MATCHING, Feature (computer vision), WARPING, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, Artificial intelligence, Image warping, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, DEFORMABLE MODEL, ComputingMilieux_MISCELLANEOUS, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

This paper describes a method to automatically generate the mapping between a completely labeled reference image and the 3D medical image of a patient. To achieve this, we combined three techniques: the extraction of 3D feature lines, their matching using 3D deformable line models, the extension of the deformation to the whole image space using warping techniques. We present experimental results for the segmentation of structures in Magnetic Resonance images of the brain of different patients; the segmentation of the cortical and ventricle structures. We emphasize the advantages of using crest lines deformable models prior to surface based models. This gives a sparser representation of the data, easier to manipulate, and which makes the convergence of the model much less sensitive to initial positionning. In the future, we hope to use this method to generate anatomical atlases, by the automatic interpretation of large sets of 3D medical images.

Published
1995

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