10 January 1997 Segmentation algorithm for image sequences from a pel-recursive motion field
Author Affiliations +
Proceedings Volume 3024, Visual Communications and Image Processing '97; (1997); doi: 10.1117/12.263195
Event: Electronic Imaging '97, 1997, San Jose, CA, United States
We present an algorithm to segment image sequences form motion information. A dense vector filed estimated by a Wiener-based pel-recursive method represents the key to separate a viewed scene into regions with different apparent displacement, according to a four-parameter motion model. A preprocessing stage using mathematical morphology enhances pel-recursive motion estimation. The proposed segmentation model, based on Markov Random Fields theory , considers-- besides the motion field--other information sources that help describe the problem more accurately. The maximum a posteriori criterion is used for the optimization of the solution, and performed with a deterministic approach. The complete segmentation algorithm includes initializing, region numbering and labeling, parameter estimation of the motion model in each region, and optimization of the segmentation field. Results on synthetic and real sequences are shown.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Gatica-Perez, Francisco Garcia-Ugalde, Victor Garcia-Garduno, "Segmentation algorithm for image sequences from a pel-recursive motion field", Proc. SPIE 3024, Visual Communications and Image Processing '97, (10 January 1997); doi: 10.1117/12.263195; https://doi.org/10.1117/12.263195

Image segmentation

Motion estimation

Motion models

Image processing algorithms and systems

Image processing

Image compression

Optimization (mathematics)

Back to Top