一种改进的特征点匹配局部遮挡目标识别算法

图像的目标识别是图像处理与模式识别领域一个重要的研究方向，它在安全监控、医疗渗断等领域得到了越来越广泛的应用,传统算法能够准确地识别背景简单、无遮挡的目标，然而对于存在局部遮挡的目标常常会出现虚警或漏报．针对这一问题，木文提出了一种改进的基于特征点匹配的目标识别算法．该算法利用harris角点检测对特征点进行初步提取，通过对已得特征点的概率密度的有效性估计来对其进行精准提取，进而实现对局部遮挡的目标的有效识别．实验表明：本文算法实时性较好，能很好的解决局部遮挡的目标识别问题．

Modified Partially-occluded Object Recognition Algorithm Based on Feature Points Matching

Image target recognition is one of the most important topics in the fields of image processing and pattern recognition. It has been more and more widely used in the fields of security monitoring, medical diagnostics and so on. Traditional algorithm can accurately identify the non-occluded target with simple background. For the partially-occluded target, however, the traditional recognition algorithms often make mistakes of false alarm and missed alarm. To solve the problems caused by partial occlusion, this paper proposes a modified target recognition algorithm, based on matching feature points. This algorithm gets the feature points roughly by corner detection of Harris, estimates the effectiveness of the feather points' probability density, therefore effectively recognize the target. The proposed algorithm has better real-time tracking, and it provides a good solution to solve the recognition of the partially-occluded target.