• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.718-723
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• 2014

In this paper, we developed the inspection system of electric vehicle battery plate using image processing. Four cameras are used for acquiring the principal parts of the plate, and several steps of image processing for extracting significant dimensions of the plate such as widths and lengths. As a preceding step, calibration of four cameras is carried for compensating distorted images using dot-arrayed sheet. Coordinate systems for four cameras are defined where one coordinate system is assigned to the reference coordinate system to which the others are relatively described. Line information of the edge in the windowed image is extracted using elaborate edge-detection algorithm, and finally the intersection points between lines are extracted to calculate widths and lengths of the plate from which the error status of the battery plate is decided.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.2
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• pp.81-87
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• 1999

In this research, we propose a field mismatch compensation method for interlaced scan image and a image restoration technique for removing motion blur. In order to compensate field mismatch, the edge classification-based linear interpolation technique and the method using the object-based motion compensation are described. We also propose an edge estimation method and an motion-based image segmentation algorithm. For removing motion blur, we adopt an adaptive iterative image restoration method using the motion-based segmentation result to improve the quality of restored image.

• 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 and Information Engineers
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• v.50 no.5
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• pp.224-230
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• 2013

Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.514-520
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• 2009

This paper proposes a new deinterlacing algorithm which improves the performance of the spatial filter. Extracting exact edges is very important element of deinterlacing performance. So the proposed algorithm has interpolated locally adaptive-thresholded binary image, and extracted exact edges from the interpolated binary image. The values of pixels on edges extracted from binary image are interpolated using neighborhood lines on the same edge. With computer simulations for a variety of images, it is shown that the proposed algorithm is much better than traditional methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.399-404
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• 2002

To restore image degraded by motion blur and additive noise, it is very difficult. In conventional restoration method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive restoration method using directional regularization operator considering edges and the regularization operator with no direction for flat regions. We verified that the proposed method showed better results in the resolution. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.680-688
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• 2010

In this paper, we present a dynamic programming(DP)-based stereo matching method using image segmentation algorithm. DP has been a classical and popular optimization method for various computer vision problems including stereo matching. However, the performance of conventional DP has not been satisfactory when it is applied to the stereo matching since the vertical correlation between scanned lines has not been properly considered. In the proposed algorithm, accurate edge information is first obtained from segmented image information then we considers the discontinuity of disparity and occlusions region based on the obtained edge information. The experimental results applied to the Middlebury stereo images demonstrate that the proposed algorithm has better performances in stereo matching than the previous DP based algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.956-958
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• 2016

Currently, image processing has been widely utilized and the noise may be occurred in the processes of image data transmission, processing, and storage. The studies have been actively conducted to eliminate the added noise in the image. The types of noise in the image are various depending on the causes and the forms, and additive white Gaussian noise(AWGN) is the representative one. The algorithm to apply and process the weighted value was suggested by the directions of the pixel in the local mask using edge detection to relieve the added AWGN in the image in this article.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1635-1644
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• 1993

In this paper, we examine the statistical characteristics of edge enhancing filters and their efficacy as preprocessing operator before edge detection. In particular, edge enhancing filters called the Comparison and Selection(CS), Hachimura-kuwahara(HK), and Selective Average(SA) filters are considered. These filters can reduce noise while producing step-type edges, thus seem to be effective for preprocessing noisy images prior to applying edge detecors. The ability of edge enhancing filters to suppress white Gaussian noise and the error probabilities occured during the edge detection following SA prefiltering are evaluated statistically through numerical analysis. The effect of prefiltering on edge detection is assessed by applying the edge enhancing fitters to a noise image degraded by additive white noise prior to applying the Sobel operator and the Laplacian of Gaussian( LoG ) operator, respectively. It is shown that the edge enhancing filters tend to produce ideal step-type edges while reducing the noise reasonably well, and the use of edge enhancing filters prior to edge detection can improve the performance of subsequent edge detector.