Texture Classification and Unsupervised Segmentation Using Directional Subband Decomposition

• Main Authors: Gian-Huei Guo, 郭建輝
• Other Authors: Wen-Rong Wu
• Format: Others
• Language: en_US
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/36295149645238906782

碩士 === 國立交通大學 === 電信研究所 === 82 === This thesis presents both texture classification and segmentation algorithms by multichannel decomposition. The channels are characterized by a bank of directional subband filters that allow a two-dimensional input signal to be represented by a sum of maximally decimated subband images and perfectly reconstructed from these decimated ones. For classification, we model the filtered channel image as Markov Random Field (MRF) and the model parameters are then extracted as texture features. For texture segmentation, four stages are taken, namely, features extraction, coarse segmentation, fine segmentation, and post processing. Correlations in each channel are used as features. At coarse segmentation stage, a fast and efficient clustering algorithm by incorporating the clusters' spatial locations is introduced. To estimate the true number of textures (cluster validility problem), we propose a new cluster- number decision algorithm by integrating each channel's clustering result. Simulation results demonstrate the effectiveness of our proposed texture classification and segmentation algorithms.