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Symmetry is an important visual characteristic in the image and can be used to identify real-world objects based on their geometrically balanced structures. Although image analysis of axisymmetry has been studied for years and many approaches have been developed to detect axisymmetry, image analysis of center-symmetry has been received little attention. Symmetrical center detection is a very important inspection for image analysis. The detected result is useful for image processing, such as object detection, image inpainting. It is difficult to detect the symmetrical center for the real-world image. Because the symmetrical objects usually appear some geometric transformation making them not perfectly center-symmetrical. This article proposes a novel weight voting approach to detect the global symmetrical center from a single image, noted as weight voting for center-symmetrical object detection (WVCS). Firstly, we sampled some feature points to vote for the global symmetrical center. Then, the orientation of Log-Gabor response and color information of HSV space are utilized as the feature descriptor to compute a similarity measure for two points. A proposed penalty term is employed to focus on the center-symmetrical objects in the foreground. The position with the maximal weight voted by all sampled feature points represents the symmetrical center. Based on the detected symmetrical object can be detected by fitting the symmetrical feature point pairs. The experimental results show that the WVCS outperforms other state-of-the-art algorithms while detecting the symmetrical center and objects from real-world images.

Center-Symmetrical Object Detection Based on Gabor Wavelets