Symmetry axis extraction is an important part of the image feature detection. So far, various classical symmetry axes extraction algorithms have been proposed, such as the minimum-inertia-axis-based method, the SIFT-based method. If the input image is blurry, or it’s difficult to extract feature points or corner points from input images, however, the above algorithms are difficult to obtain satisfied results. This paper presents a gradient-based method that can robustly extract symmetry axis from visual pattern. The key points of our methods are gradient calculation, symmetric weight calculation, and Hough Transform. Our method was evaluated on several datasets, including both blurred and smooth-edged cases. Experimental results demonstrated that our method achieves a more robust performance than previous methods.
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