一种基于数据竞争的高分辨率图像的聚类分割算法

近些年,机器人技术得到了迅猛的发展,应用越来越广泛.随着机器人技术的推广和普及,对机器人使用的要求也越来越高,其中对智能机器人的要求尤显迫切.机器视觉是智能机器人研究领域的一个重要研究方向.在机器人视觉系统中,核心问题是目标提取,对目标实时、准确、快速提取的关键技术是图像分割.由于机器人感知的环境的复杂性及目标的多样性,往往导致机器人感知获得的图像数据量较大且图像本身存在不可预知的复杂性,这就对准确的目标分割和提取处理提出了挑战性问题.本文针对高分辨率图像数据集的分割处理,提出一种新的聚类算法,即根据数据点能量和的大小识别类代表点和类成员点,通过数据点间的竞争识别出最有能力成为簇成员的数据点,并将其与mean shift聚类算法有效地结合应用于彩色图像分割问题中,能够快速高效地实现高分辨率图像的目标分割,并得到较好的图像分割效果.实验结果表明,本文算法在分割效果和分割效率上明显优于传统聚类算法.

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.