A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion Segmentation and Depth Ordering Using an Occlusion Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Edge detection or interesting point detection is one of the most fundamental methods in CV and image processing. Most previous methods have devoted to detect spatial image features. For videos with natural phenomena, not only the spatial features but also the temporal features are important to analyze and to classify a dynamic scene. In this paper, a spatio-temporal (ST) derivative tensor based on multiple frames is proposed. The spatio-temporal information containing in the multiple frames enable us to estimate the magnitude and orientation of the dynamic edges. With the estimated magnitude and orientation of dynamic edges, we can classify the dynamic scene into different regions with distinctive motion activities. The present experimental results demonstrate the method's ability to classify both rigid motions, and non-rigid motions as well when compared with some state-of-the-art techniques.