A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital video processing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Layered Motion Segmentation and Depth Ordering by Tracking Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Automatic segmentation of moving objects in video sequences: a region labeling approach
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose a method to refine a motion field from image sequences and region-based motion segmentation using the motion information. An initial motion field is generated by a block matching algorithm. We compute the motion profile at each block and define the motion confidence measure from the motion profile. In the refining process, we regulate the motion vectors with low confidence to those with high confidence. In the segmentation stage, each frame of the image sequence is partitioned into regions by a watershed algorithm and a motion vector is assigned to each region. After constructing a region adjacency graph, the graph is segmented by the normalized cuts algorithm. The experiments show that the proposed method provides satisfactory results in motion segmentation from image sequences with or without camera motion.