基于区域划分的角点检测

本文主要研究角点检测中全局/局部的搜索算法，针对该算法效率较低的情况提出了改进的角点检测算法. 该算法采用相似金字塔计算原理构造多层图像，同时采用多尺度Harris算子分层搜索并提取图像特征角点，经过分层图像、分区域图像的特征角点进行融合计算，实现了目标特征点寻找. 该算法主要在角点检测上考虑不同层次的图像和单张图像区域关系，并且通过特征点周围像素的变化参数来实现目标的定位. 实验结果表明，本文提出的改进算法提高了总体定位的速度，降低了误定位的概率.

Zoning Based on Corner Detection

This paper studies the global or local search algorithm detection, and an improved corner detection algorithm is proposed for the case of low algorithm efficiency. The algorithm uses the similar pyramid calculation principle to construct multilayer images, it also uses multi-scale Harris operator to search hierarchically and extract image feature corner. Fusion calculations are taken by the feature corner of layered images and subregional images to look for the target feature points. The algorithm mainly considers the regional relations of images between different levels and single image in corner detection. And it uses the changing parameters of pixels around the feature points to achieve the positioning of goals. Experimental results show that the proposed algorithm improves the positioning speed and reduces the probability of false positioning.