Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Canonical Frames for Planar Object Recognition
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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 Geodesic Active Contour Framework for Finding Glass
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Object detection by contour segment networks
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Lesion detection and segmentation in uterine cervix images using an ARC-level MRF
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
A fully automated approach to segmentation of irregularly shaped cellular structures in EM images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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Recent research into recognizing object classes (such as humans, cows and hands) has made use of edge features to hypothesize and localize class instances. However, for the most part, these edge-based methods operate solely on the geometric shape of edges, treating them equally and ignoring the fact that for certain object classes, the appearance of the object on the “inside” of the edge may provide valuable recognition cues. We show how, for such object classes, small regions around edges can be used to classify the edge into object or non-object. This classifier may then be used to prune edges which are not relevant to the object class, and thereby improve the performance of subsequent processing. We demonstrate learning class specific edges for a number of object classes — oranges, bananas and bottles — under challenging scale and illumination variation. Because class-specific edge classification provides a low-level analysis of the image it may be integrated into any edge-based recognition strategy without significant change in the high-level algorithms. We illustrate its application to two algorithms: (i) chamfer matching for object detection, and (ii) modulating contrast terms in MRF based object-specific segmentation. We show that performance of both algorithms (matching and segmentation) is considerably improved by the class-specific edge labelling.