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24
A survey of content based 3D shape retrieval methods
- Multimedia Tools and Applications
, 2008
"... Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve simil ..."
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Cited by 289 (1 self)
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Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape representation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) ability to perform partial matching, (6) robustness, and (7) necessity of pose normalization. Finally, the advantages and limits of the several approaches in content based 3D shape retrieval are discussed. 1.
Polyhedral Model Retrieval Using Weighted Point Sets
- International Journal of Image and Graphics
, 2002
"... Due to the recent improvements in laser scanning technology, 3D visualization and modelling, there is an increasing need for tools supporting the automatic search for 3D objects in archives. In this paper we describe a new geometric approach to 3D shape comparison and retrieval for arbitrary obje ..."
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Cited by 54 (1 self)
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Due to the recent improvements in laser scanning technology, 3D visualization and modelling, there is an increasing need for tools supporting the automatic search for 3D objects in archives. In this paper we describe a new geometric approach to 3D shape comparison and retrieval for arbitrary objects described by 3D polyhedral models that may contain gaps. In contrast with existing approaches, our approach takes the overall relative spatial location into account by representing the 3D shape as a weighted point set. To compare two objects geometrically we first apply principal components analysis to bring the objects in a standard pose, and enclose each object by a 3D grid. Then we generate for each object a signature representing a weighted point set, that contains for each non-empty grid cell a salient point. We compare three methods to select in each grid cell a salient point and a weight: (1) choose the vertex in the cell with the highest Gaussian curvature, and choose as weight a measure for that curvature, (2) choose the area-weighted mean of the vertices in the cell, and choose as weight a measure denoting the normal variation of the facets in the cell and (3) choose the centre of mass of all vertices in the cell, and choose as weight one. Finally, we compute the similarity between two shapes by comparing their signatures using a new shape similarity measure based on weight transportation that is a variation on the Earth Mover's Distance. Unlike the Earth Mover's Distance, the new shape similarity measure satisfies the triangle inequality.
Object recognition in the geometric era: A retrospective
- Toward CategoryLevel Object Recognition, volume 4170 of Lecture Notes in Computer Science
, 2006
"... Abstract. Recent advances in object recognition have emphasized the integration of intensity-derived features such as affine patches with associated geometric constraints leading to impressive performance in complex scenes. Over the four previous decades, the central paradigm of recognition was base ..."
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Cited by 29 (0 self)
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Abstract. Recent advances in object recognition have emphasized the integration of intensity-derived features such as affine patches with associated geometric constraints leading to impressive performance in complex scenes. Over the four previous decades, the central paradigm of recognition was based on formal geometric object descriptions with a focus on the properties of such descriptions under perspective image formation. This paper will review the key advances of the geometric era and investigate the underlying causes of the movement away from formal geometry and prior models towards the use of statistical learning methods based on appearance features. 1
Volumetric features for video event detection
, 2008
"... Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for eve ..."
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Cited by 21 (0 self)
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Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match the volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking up a dropped object or waving in a crowd show reliable detection with few false positives. 1.
Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition
- IEEE Trans. Neural Netw
, 2009
"... This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensor-to-vector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features direc ..."
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Cited by 20 (12 self)
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This paper proposes a novel uncorrelated multilinear discriminant analysis (UMLDA) algorithm for the challenging problem of gait recognition. A tensor-to-vector projection (TVP) of tensor objects is formulated and the UMLDA is developed using TVP to extract uncorrelated discriminative features directly from tensorial data. The small-sample-size (SSS) problem present when discriminant solutions are applied to the problem of gait recognition is discussed and a regularization procedure is introduced to address it. The effectiveness of the proposed regularization is demonstrated in the experiments and the regularized UMLDA algorithm is shown to outperform other multilinear subspace solutions in gait recognition. 1.
Scale-Space Representation of 3D Models and Topological Matching
, 2003
"... Reeb graphs have been shown to be effective for topology matching of 3D objects. Their effectiveness breaks down, however, when the individual models become very geometrically and topologically detaileas is the case for complex machined parts. The result is that Reeb graph techniques, as developed f ..."
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Cited by 14 (1 self)
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Reeb graphs have been shown to be effective for topology matching of 3D objects. Their effectiveness breaks down, however, when the individual models become very geometrically and topologically detaileas is the case for complex machined parts. The result is that Reeb graph techniques, as developed for matching general shape and computer graphics models, produce poor results when directly applied to create engineering databases.
Uncorrelated Multilinear Principal Component Analysis through Successive Variance Maximization
"... Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality ..."
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Cited by 13 (7 self)
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Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality reduction algorithm for tensorial data, named as uncorrelated multilinear PCA (UMPCA). UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. We evaluate the UMPCA on a second-order tensorial problem, face recognition, and the experimental results show its superiority, especially in lowdimensional spaces, through the comparison with three other PCA-based algorithms. 1.
Pose Estimation of Known Objects by Efficient Silhouette Matching
"... Abstract—Pose estimation is essential for automated handling of objects. In many computer vision applications only the object silhouettes can be acquired reliably, because untextured or slightly transparent objects do not allow for other features. We propose a pose estimation method for known object ..."
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Cited by 6 (1 self)
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Abstract—Pose estimation is essential for automated handling of objects. In many computer vision applications only the object silhouettes can be acquired reliably, because untextured or slightly transparent objects do not allow for other features. We propose a pose estimation method for known objects, based on hierarchical silhouette matching and unsupervised clustering. The search hierarchy is created by an unsupervised clustering scheme, which makes the method less sensitive to parametrization, and still exploits spatial neighborhood for efficient hierarchy generation. Our evaluation shows a decrease in matching time of 80 % compared to an exhaustive matching and scalability to large models. Keywords-component; formatting; style; styling;
Multilinear Subspace Learning for Face and Gait Recognition
, 2008
"... Face and gait recognition problems are challenging due to largely varying appear-ances, highly complex pattern distributions, and insufficient training samples. This dis-sertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learne ..."
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Cited by 4 (1 self)
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Face and gait recognition problems are challenging due to largely varying appear-ances, highly complex pattern distributions, and insufficient training samples. This dis-sertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for sys-tematic treatment of the multilinear subspace learning problem. Three multilinear pro-jections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear prin-cipal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear dis-criminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated mul-
A Similarity-Based Approach for Shape Classification using Aslan Skeletons
, 2010
"... Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity based approach for classifying 2D s ..."
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Cited by 2 (1 self)
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Shape skeletons are commonly used in generic shape recognition as they capture part hierarchy, providing a structural representation of shapes. However, their potential for shape classification has not been investigated much. In this study, we present a similarity based approach for classifying 2D shapes based on their Aslan skeletons [1, 2]. The coarse structure of this skeleton representation allows us to represent each shape category in the form of a reduced set of prototypical trees, offering an alternative solution to the problem of selecting the best representative examples. The ensemble of these category prototypes is then used to form a similarity based representation space in which the similarities between a given shape and the prototypes are computed using a tree edit distance algorithm, and Support Vector Machine (SVM) classifiers are used to predict the category membership of the shape based on computed similarities.
