Fundamentals of digital image processing
Fundamentals of digital image processing
MPEG-4: multimedia for our time
IEEE Spectrum
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color texture segmentation using feature distributions
Pattern Recognition Letters
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Fast image segmentation based on multi-resolution analysis and wavelets
Pattern Recognition Letters
Antiextensive connected operators for image and sequence processing
IEEE Transactions on Image Processing
Automatic segmentation of moving objects for video object plane generation
IEEE Transactions on Circuits and Systems for Video Technology
Video segmentation for content-based coding
IEEE Transactions on Circuits and Systems for Video Technology
A video segmentation algorithm for hierarchical object representations and its implementation
IEEE Transactions on Circuits and Systems for Video Technology
Efficient image segmentation for region-based motion estimation and compensation
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Improved techniques for automatic image segmentation
IEEE Transactions on Circuits and Systems for Video Technology
Spatiotemporal region enhancement and merging for unsupervized object segmentation
Journal on Image and Video Processing
A color image segmentation algorithm by using region and edge information
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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We present an efficient video segmentation and tracking strategy based on edge information to assist object-based video coding, motion estimation, and motion compensation for MPEG-4 and MPEG-7. The proposed algorithm utilizes the human visual perception to provide edge information. Three parameters are introduced and described based on edge information from the analysis of a local histogram. An edge function is defined to generate the edge information map, which can be thought as the gradient image. Then, an improved marker-based region growing and merging techniques are derived to separate the image regions. An efficient temporal segmentation and tracking algorithm is also developed in time domain when the initial segmentation is given. The proposed algorithm is tested on several standard sequences and demonstrates high reliability for video object segmentation and tracking.