Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
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
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Learning the Compositional Nature of Visual Object Categories for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using partial edge contour matches for efficient object category localization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Voting by grouping dependent parts
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Contour grouping and abstraction using simple part models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
International Journal of Computer Vision
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Shape is a natural, highly prominent characteristic of objects that human vision utilizes everyday. But despite its expressiveness, shape poses significant challenges for category-level object detection in cluttered scenes: Object form is an emergent property that cannot be perceived locally but becomes only available once the whole object has been detected and segregated from the background. Thus we address the detection of objects and the assembling of their shape simultaneously. A dictionary of meaningful contours is obtained by clustering based on contour co-activation in all training images. We seek a joint, consistent placement of all contours in an image, since placing them independently from another is not reliable due to the emergence of shape. Therefore, the characteristic object shape is learned by discovering spatially consistent configurations of all dictionary contours using maximum margin multiple instance learning. During recognition, objects are detected and their shape is explained simultaneously by optimizing a single cost function. We demonstrate the benefit of our approach on standard shape benchmarks.