This paper presents a new color image analysis approach which fuses several processing maps by Graph-Cuts algorithm for Markov Random Field (MRF) in different color spaces. Recently, graph-based image analysis methods, such as Graph-Cuts, have been achieved exciting results for approximate inference in Markov Random Field. One color image can be represented in many color spaces, such as RGB, LAB and HSV, but existing Graph-Cuts approaches often compute global energy function only in one color spaces and ignore that each color space has an interesting property for certain applications respectively. This paper processes images in MRF and represents them in one MRF model firstly. Then Graph-Cuts algorithms are used to process images in each color space and generate one map. Several processing maps can be acquired from some color spaces. These maps are fused to get more reliable and accurate results. We select stereo matching which can get depth maps from multi-view images to evaluate our image analysis approach. The experiments herein reported in this paper illustrate the potential of this approach compared to existing Graph-Cuts methods from processing results.

Date Published: 24 November 2009
PDF: 9 pages
Proc. SPIE 7513, 2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology, 75130O (24 November 2009); doi: 10.1117/12.837847

Published in SPIE Proceedings Vol. 7513:
2009 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology
Toru Yoshizawa; Ping Wei; Jesse Zheng, Editor(s)