We present a family of scale-invariant local shape features formed by chains of k connected, roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary, without including nearby clutter. Moreover, they oer an attractive compromise between information content and repeatability, and encompass a wide variety of local shape structures
International audienceWe introduce a new class of distinguished regions based on detecting the most ...
Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, ...
This paper aims at detecting objects via a partial shape matching in unlabeled real images. As both ...
We present a family of scale-invariant local shape features formed by chains of k connected, roughly...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
Humans have an amazing ability to localize and recognize object shapes from nat-ural images with var...
We present a method for object detection based on global shape. A distance measure for elastic shape...
Humans have an amazing ability to localize and recognize object shapes from natural images with vari...
Humans have an amazing ability to localize and recognize object shapes from natural images with vari...
We propose a method for object detection in cluttered real images, given a single hand-drawn example...
In this paper, we propose a novel framework for con-tour based object detection. Compared to previou...
A novel method is proposed to detect multi-part objects of unknown specific shape and appearance in ...
International audienceWe introduce a new class of distinguished regions based on detecting the most ...
Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, ...
This paper aims at detecting objects via a partial shape matching in unlabeled real images. As both ...
We present a family of scale-invariant local shape features formed by chains of k connected, roughly...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
International audienceWe present a family of scale-invariant local shape features formed by chains o...
Humans have an amazing ability to localize and recognize object shapes from nat-ural images with var...
We present a method for object detection based on global shape. A distance measure for elastic shape...
Humans have an amazing ability to localize and recognize object shapes from natural images with vari...
Humans have an amazing ability to localize and recognize object shapes from natural images with vari...
We propose a method for object detection in cluttered real images, given a single hand-drawn example...
In this paper, we propose a novel framework for con-tour based object detection. Compared to previou...
A novel method is proposed to detect multi-part objects of unknown specific shape and appearance in ...
International audienceWe introduce a new class of distinguished regions based on detecting the most ...
Due to distortion, noise, segmentation errors, overlap, and occlusion of objects in digital images, ...
This paper aims at detecting objects via a partial shape matching in unlabeled real images. As both ...