Along with the machine vision technology increasingly mature, it has been widely used in medical diagnosis, military, automatic detection and control, remote sensing, scientific research, intelligent robot and life etc. As one of key technologies, real-time performance of image matching algorithms has become one of the main considerations for practical application. In recent years, the precision and robustness of matching algorithms have always been the research focal point while the real-time performance hasn’t got enough attention. In this paper, we focus on studying and exploring how to improve real-time performance of matching algorithms. The main work can be summarized as following: (1)To acquire object images of different rotation angles, we proposed a fast-high-quality image rotation algorithm using block division technology. Comparing with basic image rotation algorithm, the runtime of our algorithm decreases significantly under same accuracy, and we proposed Dart model to eliminate float point operation. (2)To realize fast matching of untextured objects, we proposed a fast template matching algorithm based on dominant feature points, which has strong robustness to image deformation, complex background, occlusion and all kinds of disturbances. We proposed some optimization methods such as search strategy based on dominant feature points, image mapping and indexing based on dominant orientation to make algorithm achieve good real-time performance. (3)To realize fast matching of textured objects, we proposed a rotation invariant RIBRIEF descriptor based on BRIEF descriptor, which has strong robustness to illumination, noise and image blur. We proposed some optimization ways such as combining descriptor indexing with descriptor cluster to make algorithm have ideal real-time performance. Experiment results show that the matching speed of two images of size 640x480 is only 10ms by reducing the number of feature points. (4)To realize fast matching of deformed objects, we proposed a kind of image region descriptor CHRI, which is rotation invariant and strongly robust to image deformation. Some optimization methods are used to obtain good real-time performance. Experiment results show that the performance of CHIR is better than SURF in real-time and image distortion.
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