A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition. Rock particle image segmentation is typically the first and most difficult task [59]. All subsequent interpretation tasks, including particle size, shape and texture analysis, rely heavily on the quality of the segmentation results. Since rock particle images vary from one to another, it is difficult or impossible to design and develop one segmentation algorithm for all kinds of rock particle images. The presented segmentation algorithms were developed for just several types of rock particle images with a certain characteristics with respect to segmentation. In general, both two steps of rock particle image segmentation, i.e. segmentation based on gray level and segmentation based on shape and size of rock particle particles, are needed in most cases of rock particle application. The thresholding algorithms are for images where particles or particle clusters differ everywhere in intensity from the background. The thresholding algorithms are not sensitive to texture on particles, and have normally a low cost for processing. When particles are densely packed, and particles are surrounded by particles and some void spaces (background), the algorithm based on split-and-merge can be applied for the image. The algorithm based on split-and-merge has the advantage of producing higher level primitives, but the region so extracted may not correspond to actual particles, and the boundary of the extracted region is rough. The algorithm based on edge detection is suitable for the images without too much texture on the surface of particles. It has the disadvantage of producing low-level primitives (segments) even after considerable processing. As one example, the two algorithms are compared in Fig. 23. In Fig. 23(a), the original image is a This work is supported by the spanish national projects: GV06/166 and CICyT TIN2006? 14932?C02, partially supported by EU ERDF Pattern Recognition in Time-Frequency Domain: Selective Regional Correlation and Its Applications
