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—Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that previous methods have also exploited. Unlike previous… (More)

We develop a multi-class object detection framework whose core component is a nearest neighbor search over object part classes. The performance of the overall system is critically dependent on the distance measure used in the nearest neighbor search. A distance measure that minimizes the mis-classification risk for the 1-nearest neighbor search can be shown… (More)

Using a saliency measure based on the global property of contour closure, we have developed a method that reliably segments out salient contours bounding unknown objects from real edge images. The measure also incorporates the Gestalt principles of proximity and smooth continuity that previous methods have exploited. Unlike previous measures, we incorporate… (More)

- Shyjan Mahamud, Martial Hebert, Reid Simmons, Takeo Co-Chair, Jianbo Kanade, Pietro Shi +1 other
- 2002

The reliable detection of an object of interest in an input image with arbitrary background clutter and occlusion has to a large extent remained an elusive goal in computer vision. Traditional model-based approaches are inappropriate for a multi-class object detection task primarily due to difficulties in modeling arbitrary object classes. Instead, we… (More)

Astract: The estimation of the projective structure of a scene from image correspondences can be formulated as the minimization of the mean-squared distance between predicted and observed image points with respect to the projection matrices, the scene point positions, and their depths. Since these unknowns are not independent, constraints must be chosen to… (More)

The optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [5]. For implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple… (More)

We approach the task of object discrimination as that of learning efficient " codes " for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The " code " is built incrementally by successively constructing discriminants that focus on pairs of training images of objects… (More)