• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1035-1036
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• 2008

본 논문은 주행 중인 자동차에서 획득한 영상에서 배경과 도로영역 및 물체를 분리하기 위한 영역분할 기법과 물체 검출 기법을 제안하고자 한다. 영상내의 에지라인의 화소 간 연결성을 이용한 라인검출을 이용하여 도로의 윤곽선 정보를 추출하고 컬러분포를 통해 배경과 도로영역을 분리한다. 물체가 가지는 코너 특성을 이용하여 나타난 정보들의 군집화를 통해 후보영역을 얻고 컬러 성분을 이용하여 개별 물체를 분리해냈다. 제안된 알고리즘은 복잡한 배경을 갖는 도로영상의 경우에도 도로영역과 물체의 검출에 강인함을 실험을 통해 검증하였다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.109-120
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• 1998

An image segmentation approach based on the fuzzy worm searching and MIN-MAX clustering algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action and spatial relationship of the pixels as the parameters of its objective function. But the conventional segmentation methods for edge extraction generally need the mask information for the algebraic model, and take long run times at mask operation, whereas the proposed algorithm has single operation according to active searching of fuzzy worms. In addition, we also propose both genetic fuzzy worm searching and genetic min-max clustering using genetic algorithm to complete clustering and fuzzy searching on grey-histogram of image for the optimum solution, which can automatically determine the size of ranges and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.57-61
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• 1999

In the field of image compression, Classified Vector Quantization(CVQ) reveals attractive characteristics for preserving perceptual features, such as edges. However, the classification scheme is not generalized to effectively reconstruct different kinds of edge patterns in the original CVQ that predefines several linear-type edge classes: vortical edge horizontal edge diagonal edge classes. In this paper, we propose a new classification scheme, especially for edge blocks based on the similarity measure for edge patterns. An edge block is transformed to a feature vector that describes the detailed shape of the edge pattern The classes for edges are formulated automatically from the training images to result in the generalization of various shapes of edge patterns. The experimental results show the generated linear/nonlinear types of edge classes. The integrity of all the edges is faithfully preserved in the reconstructed image based on the various type of edge codebooks generated at 0.6875bpp.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.491-493
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• 1998

영상인식은 최근 시각정보의 중요성과 영상을 취득장비의 발달, 처리기술의 향상으로 여러 분야에서 그 중요성과 활용도가 급격히 증가하고 있다. 본 논문에서는 도심지 항공 영상에서 자동표적인식에 관한 문제에서 탐색 물체 주변에 건물들이 밀집되어 있고, 배경이 존재하는 경우에서 fused 분할 방법을 이용하여 기존의 에지 기준 방법인 허프 변환, 에지연결 등에서 발생하는 군집화 문제점을 해결하다. 취득환경의 차이에 다른 농도치 차이를 BCT 방법으로 정규화하여 유사도 기준치로 편차오차를 계산하여 인식하였다. 실험에서는 다양한 탐색물체를 대상으로 회전, 이동, 신축 등의 복합적인 변형에 대하여 불변적으로 인식한 결과를 보였으며, 영상 정합, 컴퓨터 비전, 영상 분석, 영상 이해등의 분야에 적용 가능성을 제시하였다.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.281-288
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• 2014

A method for edge detection using K-means clustering is proposed in this paper. The method is performed through there steps. Histogram equalizing is applied to the image for the uniformed intensity distribution. Pixels are clustered by K-means clustering technique. Then Sobel mask is applied to detect edges. Experiments showed that this method detected edges better than conventional method.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1033-1039
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• 2012

In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.202-203
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• 2009

In this paper, system divide into the edge extraction, labeling, clustering, and South Korea's traditional color combination and identifying to identify Korea's traditional colors. Edge is extracted using Canny operator. And given the label, and clustering to ensure the quality of the cluster. Finally, we identify color harmony by organizing and comparing primary color with secondary color configuration table Korea traditional color.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.786-792
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• 2010

This paper presents a real-time lane detection method which can accurately find the lane-mark boundaries in complex road environment. Unlike many existing methods that pay much attention on the post-processing stage to fit lane-mark position among a great deal of outliers, the proposed method aims at removing those outliers as much as possible at feature extraction stage, so that the searching space at post-processing stage can be greatly reduced. To achieve this goal, a grid-based morphology operation is firstly used to generate the regions of interest (ROI) dynamically, in which a directional edge-linking algorithm with directional edge-gap closing is proposed to link edge-pixels into edge-links which lie in the valid directions, these directional edge-links are then grouped into pairs by checking the valid lane-mark width at certain height of the image. Finally, lane-mark colors are checked inside edge-link pairs in the YUV color space, and lane-mark types are estimated employing a Bayesian probability model. Experimental results show that the proposed method is effective in identifying lane-mark edges among heavy clutter edges in complex road environment, and the whole algorithm can achieve an accuracy rate around 92% at an average speed of 10ms/frame at the image size of $320{\times}240$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.6
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• pp.1231-1236
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• 2006

The contrast control of images with narrow dynamic range is a simple method among enhancement methods for low intensity of image. Histogram equalization is the most common method for this purpose, which stretches the dynamic range of intensity Conventional methods would fail to enhance images with extremely dark and bright regions, because of not considering the shape of histogram. In this paper, we propose a novel adaptive histogram equalization based on histogram matching with multiple Gaussian transformation function. As a result, output images with a couple of peaks of histogram could be improved and the details such as edges in dark regions could be appeared better than conventional method subjectively.