Color and/or Texture Segmentation Using Deterministic Relaxation and Fast Marching Algorithms

The segmentation of colored texture images is considered. Either luminance, color, and/or texture features could be used for segmentation. For luminance and color the classes are described using the corresponding empirical probability distributions. The Discrete Wavelet Frames analysis is used for obtaining features of texture patterns. At a first stage, pattern analysis is performed for extracting the features using the Bhattacharya distance. Two labeling algorithms are proposed. A deterministic relaxation algorithmusinga likelihoodbased distance yields the labelingof pixels to the different color-texture patterns. In addition, a new multi-label fast marching level set algorithm is utilized for the determination of the segment boundaries.

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Spyros Liapis, Eftychios Sifakis, George Tziritas
• Keywords: Computer Vision, Corresponding Empirical Probability, ICPR 2000, Texture Features, Texture Patterns