Extension of Local Binary Pattern and Weber Local Descriptor and Color Image to Grayscale Conversion

• Main Authors: Mei-Shuo Chen, 陳玫碩
• Other Authors: Soo-Chang Pei
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/33529128534705143147

碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === With the advent of the technological era, computer vision is used widely in many fields such as face recognition, object detection, image retrieval and surveillance systems. In image processing, feature extraction is an indispensable step, which can reflect the intrinsic content (information) from the images (data). I utilized Local Binary Pattern (LBP) and Weber Local Descriptor (WLD), these two powerful descriptors to do experiments including face recognition, and Chinese Calligraphy Recognition. LBP is a spatial gray-level dependence method (co-occurrence method) and can be computed efficiently by thresholding the neighborhood of each pixel with the center pixel value to form a gray-scale invariant pattern. Weber local descriptor was inspired by Weber’s Law and was deemed to base on the fact of human perception. Besides, I revised the disadvantage of Local Binary Pattern algorithm and made a combination of conventional LBP with direction information to form a robust descriptor named “Magnitude and Direction Difference Local Binary Descriptor”. Furthermore, Local Binary Pattern was used to apply to grayscale images. I attempted to make use of the descriptor on color images. I also revised the traditional method but preserving the spirit when dealing with color images, called “HSV-LBP”. My results show that the revised version can extract clear features than previous LBP on color images. Last, I put emphasis on the topic about converting color images to grayscale images and not only preserving contrast but enhancing contrast.