On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Order Structure, Correspondence, and Shape Based Categories
Shape, Contour and Grouping in Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection by global contour shape
Pattern Recognition
Multi-stage Contour Based Detection of Deformable Objects
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Finding object categories in cluttered images using minimal shape prototypes
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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
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In this paper we introduce a new representation for shapebased object class detection. This representation is based on very sparse and slightly flexible configurations of oriented edges. An ensemble of such configurations is learnt in a boosting framework. Each edge configuration can capture some local or global shape property of the target class and the representation is thus not limited to representing and detecting visual classes that have distinctive local structures. The representation is also able to handle significant intra-class variation. The representation allows for very efficient detection and can be learnt automatically from weakly labelled training images of the target class. The main drawback of the method is that, since its inductive bias is rather weak, it needs a comparatively large training set. We evaluate on a standard database [1] and when using a slightly extended training set, our method outperforms state of the art [2] on four out of five classes.