A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting buildings in aerial images
Computer Vision, Graphics, and Image Processing
Building detection and description from a single intensity image
Computer Vision and Image Understanding
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Non-photorealistic camera: depth edge detection and stylized rendering using multi-flash imaging
ACM SIGGRAPH 2004 Papers
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Occluding contour (OC) plays important roles in many computer vision tasks. The study of using OC for visual inference tasks is however limited, partially due to the lack of robust OC acquisition technologies. In this work, benefit from a novel OC computation system, we propose applying OC information to category classification tasks. Specifically, given an image and its estimated occluding contours, we first compute a distance map with regard to the OCs. This map is then used to filter out distracting information in the image. The results are combined with standard recognition methods, bag-of-visual-words in our experiments, for category classification. In addition to the approach, we also present two OC datasets, which to the best of our knowledge are the first publicly available ones. The proposed method is evaluated on both datasets for category classification tasks. In all experiments, the proposed method significantly improves classification performances by about 10 percent.