Texture Segmentation By Iterated Slice Inverse Regression And Space-Frequency Analysis

• Main Authors: Yan, Da-Iuan, 顏大淵
• Other Authors: Henry Horng Shing Lu
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/34736109838879301632

碩士 === 國立交通大學 === 統計學類 === 86 === Texture segmentation is an important step in image analysis. While human perception has great capacity in texture segmentationand recognition, it is difficult to segment texture images automatically by computers.Hence, we are motivated to apply advanced statistical tools together withthe vision model to mimic human perception.It is aimed to extract the features of textures for further segmentationand recognition based on these techniques.Different transforms ranging from the space domain, the Fourier transform in the frequency domain, and the Gabor filter banks in the space-frequencyanalysis are considered to generate the feature vectors of textures.Dimension reduction techniques are used to find out the projecteddirections of feature vectors.Based on these projected feature vectors, classification rules are selected by the sliced inverse regression when the training set isavailable.For unsupervised segmentation, initial clustering is suggested.Then, the technique of sliced inverse regression is applied iteratively to recluster the texture image by adjusting the projected feature vectorsdynamically.The simulation and empirical studies demonstrated the feasibility ofthese new approaches.