• 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.

• Transactions on Electrical and Electronic Materials
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• v.15 no.4
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• pp.230-234
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• 2014

Automatic detection of disease helps medical institutions that are introducing digital images to read images rapidly and accurately, and is thus applicable to lesion diagnosis and treatment. The aim of this study was to apply a symmetry contribution algorithm to unsharp mask filter-applied MR images and propose an analysis technique to automatically recognize brain tumor and edema. We extracted the skull region and drawed outline of the skull in database of images obtained at P University Hospital and detected an axis of symmetry with cerebral characteristics. A symmetry contribution algorithm was then applied to the images around the axis of symmetry to observe intensity changes in pixels and detect disease areas. When we did not use the unsharp mask filter, a brain tumor was detected in 60 of a total of 95 MR images. The disease detection rate for the brain was 63.16%. However, when we used the unsharp mask filter, the tumor was detected in 87 of a total of 95 MR images, with a disease detection rate of 91.58%. When the unsharp mask filter was used in the pre-process stage, the disease detection rate for the brain was higher than when it was not used. We confirmed that unsharp mask filter can be used to rapidly and accurately to read many MR images stored in a database.

• 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 Korea Society of Computer and Information
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• v.16 no.1
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• pp.53-59
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• 2011

In many face recognition applications such as security systems, it is assumed that upright faces are given to the system. In order for the system to be used in more general environments, the system should be able to deal with the rotated faces properly. It is a generally used approach to rotate the face detection window and apply face detector repeatedly to detect a rotated face in the given image. But such an approach requires a lot of computation time. In this paper, a method of extracting the axis of symmetry for a given set of points is proposed. The axis of symmetry for the edge points in the face detection window is extracted in a way that is fast and accurate, and the face detector is applied only for that direction. It is shown that the mean and standard deviation of the symmetry detection error is $0^{\circ}$ and $3^{\circ}$ respectively, for the database used.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.542-544
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• 2005

This paper describes the detection of spatio-temporal parameter using an accelerometer and footswitches to evaluate a symmetry and balance of hemiplegic patients. We detected gait data using a 3-axis accelerometer that mounted between L3 and IA intervertebral area and footswitches made by FSR-Sensor attached insole. To minimize the error of the gait parameters to be detected incorrectly in case of using only accelerometer, we enhancement the performance of detection by measuring an accelerometer and foots witches data at the same time. So, it was possible to detect more accurate gait parameters. As a result, we can confirm the symmetry and balance of hemiplegic patients. In the future. these results could be used to evaluate the walking ability in hemiplegic patients in clinical pratice.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.1
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• pp.1-11
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• 2009

This paper proposes an algorithm capable of detecting vehicles in front or in rear using a monocular camera installed in a vehicle. The vehicle detection has been regarded as an important part of intelligent vehicle technologies. The proposed algorithm is mainly composed of two parts: 1)hypothesis generation of vehicles, and 2)hypothesis verification. The hypotheses of vehicles are generated by the analysis of vertical and horizontal edges and the detection of symmetry axis. The hypothesis verification, which determines vehicles among hypotheses, is done by the AdaBoost algorithm. The proposed algorithm is proven to be effective through experiments performed on various images captured on the roads.