Toward a computational theory of shape: an overview
ECCV 90 Proceedings of the first european conference on Computer vision
Feature extraction from faces using deformable templates
International Journal of Computer Vision
Parts of Visual Form: Computational Aspects
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
FORMS: a flexible object recognition and modeling system
International Journal of Computer Vision
A perceptual grouping hierarchy for appearance-based 3D object recognition
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Shock Graphs and Shape Matching
International Journal of Computer Vision
Efficient deformable template detection and localization without user initialization
Computer Vision and Image Understanding
On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Nonserial Dynamic Programming
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Maps of Free-Form Curve Segments via Wave Propagation
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
On the Local Form and Transitions of Symmetry Sets, Medial Axes, and Shocks
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Euler Spiral for Shape Completion
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
Symmetry-Based Indexing of Image Databases
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
A shock grammar for recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Annular symmetry operators: a method for locating and describing objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
On the Intrinsic Reconstruction of Shape from Its Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Representation and Detection of Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Deformation Invariant Image Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental learning of object detectors using a visual shape alphabet
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Augmenting Shape with Appearance in Vehicle Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Segregation of moving objects using elastic matching
Computer Vision and Image Understanding
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Skeletal Shape Abstraction from Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Dynamic programming and the graphical representation of error-correcting codes
IEEE Transactions on Information Theory
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Shape-Based Object Detection via Boundary Structure Segmentation
International Journal of Computer Vision
Matching noisy outline contours using a descriptor reduction approach
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
On the Local Form and Transitions of Pre-symmetry Sets
Journal of Mathematical Imaging and Vision
Complexity of computing distances between geometric trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Shape Codification Indexing and Retrieval Using the Quad-Tree Structure
International Journal of Computer Vision and Image Processing
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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We describe a top-down object detection and segmentation approach that uses a skeleton-based shape model and that works directly on real images. The approach is based on three components. First, we propose a fragment-based generative model for shape that is based on the shock graph and has minimal dependency among its shape fragments. The model is capable of generating a wide variation of shapes as instances of a given object category. Second, we develop a progressive selection mechanism to search among the generated shapes for the category instances that are present in the image. The search begins with a large pool of candidates identified by a dynamic programming (DP) algorithm and progressively reduces it in size by applying series of criteria, namely, local minimum criterion, extent of shape overlap, and thresholding of the objective function to select the final object candidates. Third, we propose the Partitioned Chamfer Matching (PCM) measure to capture the support of image edges for a hypothesized shape. This measure overcomes the shortcomings of the Oriented Chamfer Matching and is robust against spurious edges, missing edges, and accidental alignment between the image edges and the shape boundary contour. We have evaluated our approach on the ETHZ dataset and found it to perform well in both object detection and object segmentation tasks.