Chapters authored
Mineral Froth Image Classification and Segmentation
Accurate segmentation of froth images is always a problem in the research of floating modeling based on Machine Vision. Since a froth image is with the characteristic of complexity and diversity, it is a feasible research idea for the workflow of which the froth image is firstly classified and then segmented by the image segmentation algorithm designed for each type of froth images. This study proposes a new froth image classification algorithm. The texture feature is extracted to complete the classification. Meanwhile, an improved method based on the original valley‐edge detection algorithm is also proposed in the study. Firstly, the fractional differential is introduced to design the new valley‐edge detection templates which can extract more information on bubble edges after the enhancement of the weak edges, and finally the close bubble boundaries are obtained by carrying out the improved deburring and gap connection algorithms. Experimental results show that the new classification method can be used to distinguish the types of small, middle and large bubble images. The improved image segmentation algorithm can well reduce the problems of over‐segmentation and under‐segmentation, and it is in higher adaptability.
Part of the book: Recent Advances in Image and Video Coding
Segmentation of Fuzzy and Touching Cells Based on Modified Minimum Spanning Tree and Concave Point Detection
In order to segment fuzzy and touching cell images accurately, an improved algorithm is proposed based on minimum spanning tree (MST) and concave point detection. First, the cell images are smoothed and enhanced by a Gaussian filter. Then, the improved minimum spanning tree algorithm is used to segment the cell images. The MST algorithm is modified from three aspects, namely, weight function of edges, difference function of internal and inter region, and threshold function and parameter k. Furthermore, the problem of cell touching is solved by means of concave point detection. According to the rugged topography of touching cells, the concave points are found from the concave regions in the touching cell images, which are used to find the separation points quickly and accurately. Experimental results indicate that the new algorithm is ideal and effective.
Part of the book: Recent Advances in Image and Video Coding