基于图像尖锐度的角点匹配算法

A corner matching algorithm based on image sharpness

从多角度拍摄同一物体所得不同视角图像中的关键点的匹配对图像三维重建至关重要。为了得到精准的角点匹配对,提出了基于图像尖锐度角点匹配的新算法。该算法分为3个步骤:第1,为最大限度避免噪声的干扰,使用Canny算子先检测图像,进而使用8邻域轮廓追踪算法追踪边缘点得到边缘轮廓线。第2,计算轮廓线的尖锐度获取图像中的关键角点。第3,先粗匹配利用零均值归一化互相关法建立不同图像角点间一对多的关系,再进行精匹配采用优化后减少迭代次数的松弛迭代法得到一对一匹配点对。实验表明,该算法能够在提高运行效率的同时提高角点匹配的精准度,最终得到角点精匹配对。

It is crucial to 3D image reconstruction that matching the key points of the same object from multi-angle shots of images. We propose a new corner matching algorithm based on image sharpness to get precise corner matching pairs. The algorithm is divided into three steps. Firstly, the coarse edge information of image is gained by Canny operator. To minimize noise interference to the greatest extent, we then use the 8 neighborhood contour tracking algorithm to track the edge points to obtain the edge contour. Secondly, the sharpness of the contour lines is calculated to get the key corner points in the image. Thirdly, one-to-many matching relationships between corner points in different images are established through the zero-mean normalized cross-correlation method, thus coarse-matching of point pairs is achieved;and we adopt an optimized relaxation iteration method to reduce the number of iterations and obtain one-to-one precise-matching of point pairs. Experimental results show that the proposed algorithm is able to improve the running efficiency and accuracy of corner matching, thus realizing precise-matching of point pairs.