基于投票策略的特征点提取

特征点提取算法中存在伪角点和定位不准确的问题,导致特征点匹配率低,并且影响图像配准精度和速度.针对这一问题,提出基于投票策略的特征点提取算法.算法通过选举人投票选举出最强特征性点集,有效去除伪角点.点集中的特征点满足多重准则,特征性强度高.依据坐标选举,保证了特征点定位的准确性.在发生相似变换、亮度变化和加噪的情况下对大量图像进行了特征点提取和匹配实验,并与传统的特征点提取方法进行比较.实验结果表明,该算法提取的特征点具有更好的有效性,算法具有较强的适应性和抗噪性.

Key Point Extraction Based on Voting Strategy

False corners and inaccurate orientation in key point extraction algorithms will result in low matching rate and low image registration accuracy and speed. A new method is proposed to solve the problem. A set of strongest interest points is worked out in the algorithm to eliminate false corners. Interest points in the set have high characteristic strength and meet several criteria. The same coordinate is applied to ensure orientation accuracy. With similarity transformation， brightness change and noise， a series of images are tested with the new algorithm proposed and traditional algorithms， respectively. The results show that the new algorithm has better adaptability and anti-noise performance， and is more effective in feature points extraction.