Lu Zhimao

A data competition based clustering algorithm for large image segmentation

In recent years, robot technology has been rapidly developed and applied to every walk of life. With the promotion and popularization of the robot technology, requirement of using robot is becoming high and the demand of intelligent robots is particularly urgent. Machine vision is an important research direction in the field of intelligent robotics. In the robot vision system, the core problem is the targets extraction, and the image segmentation is the key technique of extracting targets accurately, rapidly and in real-time. Since the environment is complex and the targets are diverse, the amount of images data perceived by robots is large and the images are unpredictable. Thus, it is very important to extract and segment targets accurately. Aimed at the segmentation processing of high-resolution images, a novel clustering algorithm is proposed in this paper. According to the sum of the energy of data and their sizes, it recognizes the clustering representatives and members, and identifies the most probability members by the competition among data points. Then we apply the algorithm by combining the novel clustering algorithm into Mean Shift clustering algorithm in color image segmentation problem. The algorithm can quickly and efficiently achieve the targets segmentation of high-resolution images, and has good segmentation effect. The experiments show that the proposed approach has better clustering quality and is faster than the traditional clustering algorithm.