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D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, N. Sochen,
"Affine-invariant diffusion geometry for the analysis of deformable 3D shapes",
Proc. Computer Vision and Pattern Recognition (CVPR), 2011. Abstract: We introduce an (equi-)affine invariant diffusion geometry by which surfaces that go through squeeze and shear transformations can still be properly analyzed. The definition of an affine invariant metric enables us to construct an invariant Laplacian from which the structure of the geometry is extracted. Applications of the proposed framework demonstrate its power in generalizing and enriching the existing set of tools for shape analysis. |
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A. Kovnatsky, M. M. Bronstein, A. M. Bronstein, R. Kimmel,
"Photometric heat kernel signatures",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local heat kernel signature shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail. |
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A. Hooda, M. M. Bronstein, A. M. Bronstein, R. Horaud,
"Shape palindromes: analysis of intrinsic symmetries in 2D articulated shapes",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: Analysis of intrinsic symmetries of non-rigid and articulated shapes is an important problem in pattern recognition with numerous applications ranging from medicine to computational aesthetics. Considering articulated planar shapes as closed curves, we show how to represent their extrinsic and intrinsic symmetries as self-similarities of local descriptor sequences, which in turn have simple interpretation in the frequency domain. The problem of symmetry detection and analysis thus boils down to analysis of descriptor sequence patterns. For that purpose, we show two efficient computational methods: one based on Fourier analysis, and another on dynamic programming. Metaphorically, the later can be compared to finding palindromes in text sequences. |
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J. Aflalo, A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Deformable shape retrieval by learning diffusion kernels",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: In classical signal processing, it is common to analyze and process signals in the frequency domain, by representing the signal in the Fourier basis, and filtering it by applying a transfer function on the Fourier coefficients. In some applications, it is possible to design an optimal filter. A classical example is the Wiener filter that achieves a minimum mean squared error estimate for signal denoising. Here, we adopt similar concepts to construct optimal diffusion geometric shape descriptors. The analogy of Fourier basis are the eigenfunctions of the Laplace-Beltrami operator, in which many geometric constructions such as diffusion metrics, can be represented. By designing a filter of the Laplace-Beltrami eigenvalues, it is theoretically possible to achieve invariance to different shape transformations, like scaling. Given a set of shape classes with different transformations, we learn the optimal filter by minimizing the ratio between knowingly similar and knowingly dissimilar diffusion distances it induces. The output of the proposed framework is a filter that is optimally tuned to handle transformations that characterize the training set. |
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J. Pokrass, A. M. Bronstein, M. M. Bronstein,
"A correspondence-less approach to matching of deformable shapes",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: Finding a match between partially available deformable shapes is a challenging problem with numerous applications. The problem is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise correspondence between the two. In this paper, we introduce an alternative correspondence-less approach to matching fragments to an entire shape undergoing a non-rigid deformation. We use diffusion geometric descriptors and optimize over the integration domains on which the integral descriptors of the two parts match. The problem is regularized using the Mumford-Shah functional. We show an efficient discretization based on the Ambrosio-Tortorelli approximation generalized to triangular meshes. Experiments demonstrating the success of the proposed method are presented. |
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G. Rosman, M. M. Bronstein, A. M. Bronstein, A. Wolf, R. Kimmel,
"Group-valued regularization framework for motion segmentation of dynamic non-rigid shapes",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: Understanding of articulated shape motion plays an important role in many applications in the mechanical engineering, movie industry, graphics, and vision communities. In this paper, we study motion-based segmentation of articulated 3D shapes into rigid parts. We pose the problem as finding a group- valued map between the shapes describing the motion, and force it to be piece- wise rigid. Our computation follows the spirit of the Ambrosio-Tortorelli scheme for Mumford-Shah segmentation, with a diffusion component suited for the group nature of the motion model. Experimental results demonstrate the effectiveness of the proposed method in non-rigid motion segmentation. |
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C. Wang, M. M. Bronstein, N. Paragios, A. M. Bronstein,
"Discrete minimum distortion correspondence problems for non-rigid shape matching",
Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2011. Abstract: Similarity and correspondence are two fundamental archetype problems in shape analysis, encountered in numerous application in computer vision and pattern recognition. Many methods for shape similarity and correspondence boil down to the minimum-distortion correspondence problem, in which two shapes are endowed with certain structure, and one attempts to find the matching with smallest structure distortion between them. Defining structures invariant to some class of shape transformations results in an invariant minimum-distortion correspondence or similarity. In this paper, we model shapes using local and global structures and formulate the invariant correspondence problem as binary graph labeling. We show how different choice of structure results in invariance under various classes of deformations. |
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E. Boyer, A. M. Bronstein, M. M. Bronstein, B. Bustos, T. Darom, R. Horaud, I. Hotz, Y. Keller, J. Keustermans, A. Kovnatsky, R. Litman, J. Reininghaus, I. Sipiran, D. Smeets, P. Suetens, D. Vandermeulen, A. Zaharescu, V. Zobel,
"SHREC 2011: robust feature detection and description benchmark",
Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2011. Abstract: Feature-based approaches have recently become very popular in computer vision and image analysis application, and are becoming a promising direction in shape retrieval applications. SHREC'10 robust feature detection and description benchmark simulates feature detection and description stage of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of different transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and what is the strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results. SHREC'11 feature detection benchmark |
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F. Michel, M. M. Bronstein, A. M. Bronstein, N. Paragios,
"Boosted metric learning for 3D multi-modal deformable registration",
Proc. Intl. Symposium on Biomedical Imaging (ISBI), 2011. Abstract: Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method. |
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D. Raviv, M. M. Bronstein, A. M. Bronstein, R. Kimmel,
"Volumetric heat kernel signatures",
Proc. Intl. Workshop on 3D Object Retrieval, ACM Multimedia, 2010. Abstract: Invariant shape descriptors are instrumental in numerous shape analysis tasks including deformable shape comparison, registration, classification, and retrieval. Most existing constructions model a 3D shape as a two-dimensional surface describing the shape boundary, typically represented as a triangular mesh or a point cloud. Using intrinsic properties of the surface, invariant descriptors can be designed. One such example is the recently introduced heat kernel signature, based on the Laplace-Beltrami operator of the surface. In many applications, however, a volumetric shape model is more natural and convenient. Moreover, modeling shape deformations as approximate isometries of the volume of an object, rather than its boundary, better captures natural behavior of non-rigid deformations in many cases. Here, we extend the idea of heat kernel signature to robust isometry-invariant volumetric descriptors, and show their utility in shape retrieval. The proposed approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark. |
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N. Mitra, A. M. Bronstein, M. M. Bronstein,
"Intrinsic regularity detection in 3D geometry",
Proc. European Conf. Computer Vision (ECCV), 2010. Abstract: Automatic detection of symmetries, regularity, and repetitive structures in 3D geometry is a fundamental problem in shape analysis and pattern recognition with applications in computer vision and graphics. Especially challenging is to detect intrinsic regularity, where the repetitions are on an intrinsic grid, without any apparent Euclidean pattern to describe the shape, but rising out of (near) isometric deformation of the underlying surface. In this paper, we employ multidimensional scaling to reduce the problem of intrinsic structure detection to a simpler problem of 2D grid detection. Potential 2D grids are then identified using an autocorrelation analysis, refined using local fitting, validated, and finally projected back to the spatial domain. We test the detection algorithm on a variety of scanned plaster models in presence of imperfections like missing data, noise and outliers. We also present a range of applications including scan completion, shape editing, super-resolution, and structural correspondence. |
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A. M. Bronstein, M. M. Bronstein,
"Spatially-sensitive affine-invariant image descriptors",
Proc. European Conf. Computer Vision (ECCV), 2010. Abstract: Invariant image descriptors play an important role in many computer vision and pattern recognition problems such as image search and retrieval. A dominant paradigm today is that of "bags of features", a representation of images as distributions of primitive visual elements. The main disadvantage of this approach is the loss of spatial relations between features, which often carry important information about the image. In this paper, we show how to construct spatially-sensitive image descriptors in which both the features and their relation are affine-invariant. Our construction is based on a vocabulary of pairs of features coupled with a vocabulary of invariant spatial relations between the features. Experimental results show the advantage of our approach in image retrieval applications. |
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M. M. Bronstein, I. Kokkinos,
"Scale-invariant heat kernel signatures for non-rigid shape recognition",
Proc. Computer Vision and Pattern Recognition (CVPR), 2010. Abstract: One of the biggest challenges in non-rigid shape retrieval and comparison is the design of a shape descriptor that would maintain invariance under a wide class of transformations the shape can undergo. Recently, heat kernel signature was introduced as an intrinsic local shape descriptor based on diffusion scale-space analysis. In this paper, we develop a scale-invariant version of the heat kernel descriptor. Our construction is based on a logarithmically sampled scale-space in which shape scaling corresponds, up to a multiplicative constant, to a translation. This translation is undone using the magnitude of the Fourier transform. The proposed scale-invariant local descriptors can be used in the bag-of-features framework for shape retrieval in the presence of transformations such as isometric deformations, missing data, topological noise, and global and local scaling. We get significant performance improvement over state-of-the-art algorithms on recently established non-rigid shape retrieval benchmarks. CVPR trailer video |
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M. M. Bronstein, A. M. Bronstein, F. Michel, N. Paragios,
"Data fusion through cross-modality metric learning using similarity-sensitive hashing",
Proc. Computer Vision and Pattern Recognition (CVPR), 2010. Abstract: Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images. CVPR trailer video |
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A. M. Bronstein, M. M. Bronstein, U. Castellani, B. Falcidieno, A. Fusiello, A. Godil,
L. J. Guibas, I. Kokkinos, Z. Lian, M. Ovsjanikov, G. Patané, M. Spagnuolo, R. Toldo,
"SHREC 2010: robust large-scale shape retrieval benchmark",
Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010. Abstract: SHREC'10 robust large-scale shape retrieval benchmark simulates a retrieval scenario, in which the queries include multiple modifications and transformations of the same shape. The benchmark allows evaluating how algorithms cope with certain classes of transformations and what is the strength of the transformations that can be dealt with. The present paper is a report of the SHREC'10 robust large-scale shape retrieval benchmark results. SHREC'10 shape retrieval benchmark |
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A. M. Bronstein, M. M. Bronstein, B. Bustos, U. Castellani, M. Crisani, B. Falcidieno,
L. J. Guibas, I. Kokkinos, V. Murino, M. Ovsjanikov, G. Patané, I. Sipiran, M. Spagnuolo, J. Sun,
"SHREC 2010: robust feature detection and description benchmark",
Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010. Abstract: Feature-based approaches have recently become very popular in computer vision and image analysis application, and are becoming a promising direction in shape retrieval applications. SHREC'10 robust feature detection and description benchmark simulates feature detection and description stage of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of different transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and what is the strength of the transformations that can be dealt with. The present paper is a report of the SHREC'10 robust feature detection and description benchmark results. SHREC'10 feature detection benchmark |
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A. M. Bronstein, M. M. Bronstein, U. Castellani, A. Dubrovina, L. J. Guibas, R. P. Horaud, R. Kimmel,
D. Knossow, E. von Lavante, D. Mateus, M. Ovsjanikov, A. Sharma,
"SHREC 2010: robust correspondence benchmark",
Proc. EUROGRAPHICS Workshop on 3D Object Retrieval (3DOR), 2010. Abstract: SHREC'10 robust correspondence benchmark simulates a one-to-one shape matching scenario, in which one of the shapes undergoes multiple modifications and transformations. The benchmark allows evaluating how correspondence algorithms cope with certain classes of transformations and what is the strength of the transformations that can be dealt with. The present paper is a report of the SHREC'10 robust correspondence benchmark results. SHREC'10 correspondence benchmark |
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D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, G. Sapiro,
"Diffusion symmetries of non-rigid shapes",
Proc. Intl. Symposium on 3D Data Processing, Visualization and Transmission (3DPVT), 2010. Abstract: Detection and modeling of self-similarity and symmetry is important in shape recognition, matching, synthesis, and reconstruction. While the detection of rigid shape symmetries is well-established, the study of symmetries in non- rigid shapes is a much less researched problem. A particularly challenging setting is the detection of symmetries in non-rigid shapes affected by topological noise and asymmetric connectivity. In this paper, we treat shapes as metric spaces, with the metric induced by heat diffusion properties, and define non-rigid symmetries as self-isometries with respect to the diffusion metric. Experimental results show the advantage of the diffusion metric over the previously proposed geodesic metric for exploring intrinsic symmetries of bendable shapes with possible topological irregularities. |
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M. Ovsjanikov, A. M. Bronstein, M. M. Bronstein, L. J. Guibas,
"ShapeGoogle: a computer vision approach for invariant shape retrieval",
Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009. Abstract: Feature-based methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approaches in the 3D world applied to the problem of non-rigid shape search and retrieval in large databases. |
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Y. Devir, G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"On reconstruction of non-rigid shapes with intrinsic regularization",
Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2009. Abstract: Shape-from-X is a generic type of inverse problems in computer vision, in which a shape is reconstructed from some measurements. A specially challenging setting of this problem is the case in which the reconstructed shapes are non-rigid. In this paper, we propose a framework for intrinsic regularization of such problems. The assumption is that we have the geometric structure of a shape which is intrinsically (up to bending) similar to the one we would like to reconstruct. For that goal, we formulate a variation with respect to vertex coordinates of a triangulated mesh approximating the continuous shape. The numerical core of the proposed method is based on differentiating the fast marching update step for geodesic distance computation. |
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O. Rubinstein, Y. Honen, A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"3D color video camera",
Proc. Workshop on 3D Digital Imaging and Modeling (3DIM), 2009. Abstract: We introduce a design of a coded light-based 3D color video camera optimized for build up cost as well as accuracy in depth reconstruction and acquisition speed. The components of the system include a monochromatic camera and an off-the-shelf LED projector synchronized by a miniature circuit. The projected patterns are captured and processed at a rate of 200 fps and allow for real-time reconstruction of both depth and color at video rates. The reconstruction and display are performed at around 30 depth profiles and color texture per second using a graphics processing unit (GPU). |
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A. M. Bronstein, M. M. Bronstein,
"Regularized partial matching of rigid shapes",
Proc. European Conf. Computer Vision (ECCV), pp. 143-154, 2008. Abstract: Matching of rigid shapes is an important problem in numerous applications across the boundary of computer vision, pattern recognition and computer graphics communities. A particularly challenging setting of this problem is partial matching, where the two shapes are dissimilar in general, but have significant similar parts. In this paper, we show a rigorous approach allowing to find matching parts of rigid shapes with controllable size and regularity. The regularity term we use is similar to the spirit of the Mumford-Shah functional, extended to non-Euclidean spaces. Numerical experiments show that the regularized partial matching produces better results compared to the non-regularized one. |
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A. M. Bronstein, M. M. Bronstein,
"Not only size matters: regularized partial matching of nonrigid shapes",
Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA), 2008. Abstract: Partial matching is probably one of the most challenging problems in nonrigid shape analysis. The problem consists of matching similar parts of shapes that are dissimilar on the whole and can assume different forms by undergoing nonrigid deformations. Conceptually, two shapes can be considered partially matching if they have significant similar parts, with the simplest definition of significance being the size of the parts. Thus, partial matching can be defined as a multcriterion optimization problem trying to simultaneously maximize the similarity and the size of these parts. In this paper, we propose a different definition of significance, taking into account the regularity of parts besides their size. The regularity term proposed here is similar to the spirit of the Mumford-Shah functional. Numerical experiments show that the regularized partial matching produces semantically better results compared to the non-regularized one. |
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R. Giryes, A. M. Bronstein, Y. Moshe, M. M. Bronstein,
"Embedded System for 3D Shape Reconstruction",
In Proc. European DSP Education and Research Symposium (EDERS), 2008. Abstract: Many applications that use three-dimensional scanning require a low cost, accurate and fast solution. This paper presents a fixed-point implementation of a real time active stereo threedimensional acquisition system on a Texas Instruments DM6446 EVM board which meets these requirements. A time-multiplexed structured light reconstruction technique is described and a fixed point algorithm for its implementation is proposed. This technique uses a standard camera and a standard projector. The fixed point reconstruction algorithm runs on the DSP core while the ARM controls the DSP and is responsible for communication with the camera and projector. The ARM uses the projector to project coded light and the camera to capture a series of images. The captured data is sent to the DSP. The DSP, in turn, performs the 3D reconstruction and returns the results to the ARM for storing. The inter-core communication is performed using the xDM interface and VISA API. Performance evaluation of a fully working prototype proves the feasibility of a fixed-point embedded implementation of a real time three-dimensional scanner, and the suitability of the DM6446 chip for such a system. |
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G. Rosman, A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Topologically constrained isometric embedding",
In Human Motion Understanding, Modelling, Capture, and Animation, Computational Imaging and Vision, Vol. 36, Springer, pp. 243-262, 2008. Abstract: We present a new algorithm for nonlinear dimensionality reduction that consistently uses global information, which enables understanding the intrinsic geometry of non-convex manifolds. Compared to methods that consider only local information, our method appears to be more robust to noise. We demonstrate the performance of our algorithm and compare it to state-of-the-art methods on synthetic as well as real data. |
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D. Raviv, A. M. Bronstein, M. M. Bronstein, R. Kimmel, "Symmetries of non-rigid shapes", Proc. Workshop on Non-rigid Registration and Tracking through Learning (NRTL), 2007. Abstract: Symmetry and self-similarity is the cornerstone of Nature, exhibiting itself through the shapes of natural creations and ubiquitous laws of physics. Since many natural objects are symmetric, the absence of symmetry can often be an indication of some anomaly or abnormal behavior. Therefore, detection of asymmetries is important in numerous practical applications, including crystallography, medical imaging, and face recognition, to mention a few. Conversely, the assumption of underlying shape symmetry can facilitate solutions to many problems in shape reconstruction and analysis. Traditionally, symmetries are described as extrinsic geometric properties of the shape. While being adequate for rigid shapes, such a description is inappropriate for non-rigid ones. Extrinsic symmetry can be broken as a result of shape deformations, while its intrinsic symmetry is preserved. In this paper, we pose the problem of finding intrinsic symmetries of non-rigid shapes and propose an efficient method for their computation. |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel, "Rock, Paper, and Scissors: extrinsic vs. intrinsic similarity of non-rigid shapes", Proc. Intl. Conf. Computer Vision (ICCV), 2007. Abstract: This paper explores similarity criteria between non-rigid shapes. Broadly speaking, such criteria are divided into intrinsic and extrinsic, the first referring to the metric structure of the objects and the latter to the geometry of the shapes in the Euclidean space. Both criteria have their advantages and disadvantages; extrinsic similarity is sensitive to non-rigid deformations of the shapes, while intrinsic similarity is sensitive to topological noise. Here, we present an approach unifying both criteria in a single distance. Numerical results demonstrate the robustness of our approach in cases where using only extrinsic or intrinsic criteria fail. |
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A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, "Paretian similarity for partial comparison of non-rigid objects", Proc. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), pp. 264-275, 2007. Abstract: In this paper, we address the problem of partial comparison of non-rigid objects. We introduce a new class of set-valued distances, related to the concept of Pareto optimality in economics. Such distances allow to capture intrinsic geometric similarity between parts of non-rigid objects, obtaining semantically meaningful comparison results. The numerical implementation of our method is computationally efficient and is similar to GMDS, a multidimensional scaling-like continuous optimization problem. |
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A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel, "Partial similarity of objects and text sequences", Proc. Information Theory and Applications Workshop, San Diego, 2007. Abstract: Similarity is one of the most important abstract concepts in the human perception of the world. In computer vision, numerous applications deal with comparing objects observed in a scene with some a priori known patterns. Often, it happens that while two objects are not similar, they have large similar parts, that is, they are partially similar. Here, we present a novel approach to quantify this semantic definition of partial similarity using the notion of Pareto optimality. We exemplify our approach on the problems of recognizing non-rigid objects and analyzing text sequences. |
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A. M. Bronstein, M. M. Bronstein, A. M. Bruckstein, R. Kimmel,
"Matching two-dimensional articulated shapes using generalized multidimensional scaling",
Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), pp. 48-57, 2006. Abstract: We present a theoretical and computational framework for matching of two-dimensional articulated shapes. Assuming that articulations can be modeled as near-isometries, we show an axiomatic construction of an articulation-invariant distance between shapes, formulated as a generalized multidimensional scaling (GMDS) problem and solved efficiently. Some numerical results demonstrating the accuracy of our method are presented. 2D tools dataset |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Face2Face: an isometric model for facial animation",
Proc. Conf. on Articulated Motion and Deformable Objects (AMDO), pp. 38-47, 2006. Abstract: A geometric framework for finding intrinsic correspondence between animated 3D faces is presented. We model facial expressions as isometries of the facial surface and find the correspondence between two faces as the minimum-distortion mapping. Generalized multidimensional scaling is used for this goal. We apply our approach to texture mapping onto 3D video, expression exaggeration and morphing between faces. 3D face video |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Robust expression-invariant face recognition from partially missing data",
Proc. European Conf. on Computer Vision (ECCV), pp. 396-408, 2006. Abstract: Recent studies on three-dimensional face recognition proposed to model facial expressions as isometries of the facial surface. Based on this model, expression-invariant signatures of the face were constructed by means of approximate isometric embedding into flat spaces. Here, we apply a new method for measuring isometry-invariant similarity between faces by embedding one facial surface into another. We demonstrate that our approach has several significant advantages, one of which is the ability to handle partially missing data. Promising face recognition results are obtained in numerical experiments even when the facial surfaces are severely occluded. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky,
"On separation of semitransparent dynamic images from static background",
Proc. Intl. Conf. on Independent Component Analysis and Blind Signal Separation, pp. 934-940, 2006. Abstract: Presented here is the problem of recovering a dynamic image superimposed on a static background. Such a problem is ill-posed and may arise e.g. in imaging through semireflective media, in separation of an illumination image from a reflectance image, in imaging with diffraction phenomena, etc. In this work we study regularization of this problem in spirit of Total Variation and general sparsifying transformations. |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Expression-invariant face recognition via spherical embedding",
Proc. Intl. Conf. on Image Processing (ICIP), Vol. 3, pp. 756-759, 2005. Abstract: Recently, it was proven empirically that facial expressions can be modelled as isometries, that is, geodesic distances on the facial surface were shown to be significantly less sensitive to facial expressions compared to Euclidean ones. Based on this assumption, the 3DFACE face recognition system was built. The system efficiently computes expression invariant signatures based on isometry-invariant representation of the facial surface. One of the crucial steps in the recognition system was embedding of the face geometric structure into a Euclidean (flat) space. Here, we propose to replace the flat embedding by a spherical one to construct isometric invariant representations of the facial image. We refer to these new invariants as spherical canonical images. Compared to its Euclidean counterpart, spherical embedding leads to notably smaller metric distortion. We demonstrate experimentally that representations with lower embedding error lead to better recognition. In order to efficiently compute the invariants we introduce a dissimilarity measure between the spherical canonical images based on the spherical harmonic transform. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Unmixing tissues: sparse component analysis in multi-contrast MRI",
Proc. Intl. Conf. on Image Processing (ICIP), Vol. 2, pp. 1282-1285, 2005. Abstract: We pose the problem of tissue classification in MRI as a blind source separation (BSS) problem and solve it by means of sparse component analysis (SCA). Assuming that most MR images can be sparsely represented, we consider their optimal sparse representation. Sparse components define a physically-meaningful feature space for classification. We demonstrate our approach on simulated and real multi-contrast MRI data. The proposed framework is general in that it is applicable to other modalities of medical imaging as well, whenever the linear mixing model is applicable. |
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M. M. Bronstein, A. M. Bronstein, R. Kimmel, I. Yavneh,
"A multigrid approach for multi-dimensional scaling",
Proc. Copper Mountain Conf. Multigrid Methods, 2005. Best Paper Award. Abstract: A multigrid approach for the efficient solution of large-scale multidimensional scaling (MDS) problems is presented. The main motivation is a recent application of MDS to isometry-invariant representation of surfaces, in particular, for expression-invariant recognition of human faces. Simulation results show that the proposed approach significantly outperforms conventional MDS algorithms. Multigrid MDS code | Tutorial |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Isometric embedding of facial surfaces into $S^3$",
Proc. Intl. Conf. on Scale Space and PDE Methods in Computer Vision, pp. 622-631, 2005. Abstract: The problem of isometry-invariant representation and comparison of surfaces is of cardinal importance in pattern recognition applications dealing with deformable objects. Particularly, in three-dimensional face recognition treating facial expressions as isometries of the facial surface allows to perform robust recognition insensitive to expressions. Isometry-invariant representation of surfaces can be constructed by isometrically embedding them into some convenient space, and carrying out the comparison in that space. Presented here is a discussion on isometric embedding into $S^3$, which appears to be superior over the previously used Euclidean space in sense of the representation accuracy. |
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A. M. Bronstein, M. M. Bronstein, E. Gordon, R. Kimmel,
"Fusion of 2D and 3D data in three-dimensional face recognition",
Proc. Intl. Conf. on Image Processing (ICIP), pp. 87-90, 2004. Abstract: We discuss the synthesis between the 3D and the 2D data in three-dimensional face recognition. We show how to compensate for the illumination and facial expressions using the 3D facial geometry and present the approach of canonical images, which allows to incorporate geometric information into standard face recognition approaches. |
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M. M. Bronstein, A. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Optimal sparse representations for blind source separation and blind deconvolution: a learning approach", Proc. Intl. Conf. on Image Processing (ICIP), pp. 1815-1818, 2004. Abstract: We present a generic approach, which allows to adapt sparse blind deconvolution and blind source separation algorithms to arbitrary sources. The key idea is to bring the problem to the case in which the underlying sources are sparse by applying a sparsifying transformation on the mixtures. We present simulation results and show that such transformation can be found by training. Properties of the optimal sparsifying transformation are highlighted by an example with aerial images. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Fast relative Newton algorithm for blind deconvolution of images", Proc. Intl. Conf. on Image Processing (ICIP), pp. 1233-1236, 2004. Abstract: We present an efficient Newton-like algorithm for quasimaximum likelihood (QML) blind deconvolution of images. This algorithm exploits the sparse structure of the Hessian. An optimal distribution-shaping approach by means of sparsification allows one to use simple and convenient sparsity prior for processing of a wide range of natural images. Simulation results demonstrate the efficiency of the proposed method. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky,
"Blind source separation using the block-coordinate relative Newton method",
Proc. Intl. Conf. on Independent Component Analysis and Blind Signal Separation, Lecture Notes in Comp. Science No. 3195, Springer, pp. 406-413, 2004. Abstract: Presented here is a generalization of the modified relative Newton method, recently proposed by Zibulevsky for quasi-maximum likelihood blind source separation. Special structure of the Hessian matrix allows to perform block-coordinate Newton descent, which significantly reduces the algorithm computational complexity and boosts its performance. Simulations based on artificial and real data show that the separation quality using the proposed algorithm outperforms other accepted blind source separation methods. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"QML blind deconvolution: asymptotic analysis",
Proc. Intl. Conf. on Independent Component Analysis and Blind Signal Separation, Lecture Notes in Comp. Science No. 3195, Springer, pp. 677-684, 2004. Abstract: Blind deconvolution is considered as a problem of quasi maximum likelihood (QML) estimation of the restoration kernel. Simple closed-form expressions for the asymptotic estimation error are derived. The asymptotic performance bounds coincide with the Cramér-Rao bounds, when the true ML estimator is used. Conditions for asymptotic stability of the QML estimator are derived. Special cases when the estimator is super-efficient are discussed. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Optimal sparse representations for blind deconvolution of images",
Proc. Intl. Conf. on Independent Component Analysis and Blind Signal Separation, Lecture Notes in Comp. Science No. 3195, Springer, pp. 500-507, 2004. Abstract: The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources.We propose a method of sparsification, which allows blind deconvolution of sources with arbitrary distribution, and show how to find optimal sparsifying transformations by training. |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel, A. Spira,
"Face recognition from facial surface metric",
Proc. European Conf. on Computer Vision (ECCV), pp. 225-237, 2004. Abstract: Recently, a 3D face recognition approach based on geometric invariant signatures, has been proposed. The key idea is a representation of the facial surface, invariant to isometric deformations, such as those resulting from facial expressions. One important stage in the construction of the geometric invariants involves in measuring geodesic distances on triangulated surfaces, which is carried out by the fast marching on triangulated domains algorithm. Proposed here is a method that uses only the metric tensor of the surface for geodesic distance computation. That is, the explicit integration of the surface in 3D from its gradients is not needed for the recognition task. It enables the use of simple and cost-efficient 3D acquisition techniques such as photometric stereo. Avoiding the explicit surface reconstruction stage saves computational time and reduces numerical errors. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Quasi maximum likelihood blind deconvolution of images acquired through scattering media",
Proc. Intl. Symposium on Biomedical Imaging (ISBI), pp. 352-355, 2004. Abstract: We address the problem of restoration of images obtained through a scattering medium. We present an efficient quasi-maximum likelihood blind deconvolution approach based on the fast relative Newton algorithm and optimal distributionshaping approach (sparsification), which allows to use simple and convenient sparsity prior for a wide class of images. Simulation results prove the efficiency of the proposed method. |
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A. M. Bronstein, M. M. Bronstein, R. Kimmel,
"Expression-invariant 3D face recognition",
Proc. Audio- and Video-based Biometric Person Authentication (AVBPA), Lecture Notes in Comp. Science No. 2688, Springer, pp. 62-69, 2003. Abstract: We present a novel 3D face recognition approach based on geometric invariants introduced by Elad and Kimmel. The key idea of the proposed algorithm is a representation of the facial surface, invariant to isometric deformations, such as those resulting from different expressions and postures of the face. The obtained geometric invariants allow mapping 2D facial texture images into special images that incorporate the 3D geometry of the face. These signature images are then decomposed into their principal components. The result is an efficient and accurate face recognition algorithm that is robust to facial expressions. We demonstrate the results of our method and compare it to existing 2D and 3D face recognition algorithms. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi,
"Separation of semireflective layers using Sparse ICA",
Proc. Intl. Conf. on Acoustics Speech and Signal Processing (ICASSP), Vol. 3, pp. 733-736, 2003. Abstract: We address the problem of Blind Source Separation (BSS) of superimposed images and, in particular, consider the recovery of a scene recorded through a semirefective medium (e.g. glass windshield) from its mixture with a virtual reflected image. We extend the Sparse ICA (SPICA) approach to BSS and apply it to the separation of the desired image from the superimposed images, without having any a priory knowledge about its structure and/or statistics. Advances in the SPICA approach are discussed. Simulations and experimental results illustrate the efficiency of the proposed approach, and of its specific implementation in a simple algorithm of a low computational cost. The approach and the algorithm are generic in that they can be adapted and applied to a wide range of BSS problems involving one-dimensional signals or images. |
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M. M. Bronstein, A. M. Bronstein, M. Zibulevsky,
"Iterative reconstruction in diffraction tomography using non-uniform fast Fourier transform",
Proc. Intl. Symposium on Biomedical Imaging (ISBI), pp. 633-636, 2002. Abstract: We show an iterative reconstruction framework for diffraction ultrasound tomography. The use of broadband illumination allows the number of projections to be reduced significantly compared to straight ray tomography. The proposed algorithm makes use of fast forward non-uniform Fourier transform (NUFFT) for iterative Fourier inversion. Incorporation of total variation regularization allows noise and Gibbs phenomena to be reduced whilst preserving the edges. |
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A. M. Bronstein, M. M. Bronstein, M. Zibulevsky, Y. Y. Zeevi, "Optimal nonlinear estimation of photon coordinates in PET",
Proc. Intl. Symposium on Biomedical Imaging (ISBI), pp. 541-544, 2002. Abstract: We consider detection of high-energy photons in PET using thick scintillation crystals. Parallax effect and multiple Compton interactions in this type of crystals significantly reduce the accuracy of conventional detection methods. In order to estimate the scintillation point coordinates based on photomultiplier responses, we use asymptotically optimal nonlinear techniques, implemented by feed-forward neural networks, radial basis functions (RBF) networks, and neuro-fuzzy systems. Incorporation of information about angles of incidence of photons, significantly improves accuracy of estimation. The proposed estimators are fast enough to perform detection, using conventional computers. |
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