• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.9-15
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• 2016

Symmetry is everywhere in the world around us from galaxy to microbes. From ancient times symmetry is considered to be a reflection of the harmony of universe. Symmetry is not only a significant clue for human cognitive process, but also useful information for computer vision such as image understanding system. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, analysis of medical images, and so on. The technique used in this paper extracts edges, and the perpendicular bisector of any two edge points is considered to be a candidate axis of symmetry. The coefficients of candidate axis are accumulated in the coefficient space. Then the axis of symmetry is determined to be the line for which the coefficient histogram has maximum value. In this paper, an improved method is proposed that utilizes the directional information of edges, which is a byproduct of the edge detection process. Experiment on 20 test images shows that the proposed method performs 22.7 times faster than the original method. In another test on 5 images with 4% salt-and-pepper noise, the proposed method detects the symmetry successfully, while the original method fails. This result reveals that the proposed method enhances the speed and accuracy of detection process at the same time.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.31-37
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• 2015

Symmetry is easily found in animals and plants as well as in artificial structures. It is useful not only for human cognitive process but also for image understanding by computer. Application areas include face detection and recognition, indexing of image database, image segmentation and detection, and analysis of medical images. The method used in this paper extracts edges, and the perpendicular bisector of any pair of selected edge points is considered to be a candidate axis of symmetry. The coefficients of the perpendicular bisectors are accumulated in the coefficient space. Axis of symmetry is determined to be the line for which the histogram has maximum value. This method shows good results, but the usefulness of the method is restricted because the amount of computation increases proportional to the square of the number of edges. In this paper, an acceleration method is proposed which performs $2^{2n}$ times faster than the original one. Experiment on 20 test images shows that the proposed method using level-3 image segmentation performs 63.9 times faster than the original method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.411-421
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• 2001

The generalized symmetry transform evaluates symmetry without segmentation and extracts regions of interest in an image by combining locality and reflectional symmetry The demand that the symmetry transform be local is reflected by the distance weight function. When calculating large regions-of-interest, we should select a large standard deviation of distance weight function. But such a large standard deviation makes the execution time increase in the second power of r, which is a radius of search area. In this paper we propose modified scan line based GST with selectively directional attention to improve time complexity The symmetry map of our proposed GST is found to be very similar to that of the existing GST. However the computation time of the proposed GST increases linearly with respect to r because our proposed GST evaluates symmetry between a pair of edge pixels along the scan lines. The GST computation decreases considerably when the proposed GST is peformed with selectively directional attention in case of large r. Several experiments in this paper demonstrate the time efficiency and the usefulness of our proposed GST.