• 대한전자공학회논문지SP
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• 제48권3호
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• pp.99-105
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• 2011

본 논문에서는 수정된 segment maxima을 기반으로 한 색역 추정 방법을 제안한다. Segment maxima은 CIELab 공간을 일정한 간격으로 분할하고, 각 분할된 영역의 최외각 측정 데이터를 계산하여 색역을 추정하는 기법이다. 그러나 이 방법은 색공간의 분할 개수에 따라서 지역적인 색역 왜곡이 나타나 색역 사상시에 컬러 결점(color artifact)을 발생시킨다. 색공간 분할 개수가 적으면 높은 채도 부근에서 추정된 색역 정보가 분실되어 컬러 컨투어(color contour) 현상이 발생한다. 이와 반대로 색공간 분할 개수가 많으면 CIELab 밝기 축 부근에서 색역이 오목하게 되는 왜곡이 발생한다. 이러한 지역적인 색역 왜곡은 색역 사상시 결과 영상에 컬러 결점이 유발한다. 제안한 논문에서는 실험을 통하여 측정한 데이터 수에 따른 적절한 색공간 분할개수를 설정하여 높은 채도 부근에 색역 축소를 줄이며, 오목하게 나타나는 지역적인 색역 왜곡을 다양한 색역 분할에 따른 색역 경계 서술자(gamut boundary descriptor) 비교를 통해 이를 수정하는 방법을 제안하였다. 실험을 통해 제안한 방법이 기존의 segment maxima보다 정확한 장치의 색역을 추정할 수가 있었고, 결과 영상에서도 컬러 컨투어나 반점과 같은 컬러 결점이 줄어듬을 확인 할 수 있었다.

• 전자공학회논문지B
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• 제33B권3호
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• pp.79-87
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• 1996

A new method for recognizing and locating partially occluded or overlapped two-dimensional objects regardless of their size, translation, and rotation, is presented. Dominant points approximating occuluding contoures of objects are generated by finding local maxima of smoothed k-cosine function, and then used to guide the contour segment matching procedure. Primitives between the dominant points are produced by projecting the local contours onto the line between the dominant points. Robust classification of primitives. Which is crucial for reliable partial shape matching, is performed using adaptive resonance theory (ART2). The matched primitives having similar scale factors and rotation angles are detected in the hough space to identify the presence of the given model in the object scene. Finally the translation vector is estimated by minimizing the mean squred error of the matched contur segment pairs. This model-based matching algorithm may be used in diveerse factory automation applications since models can be added or changed simply by training ART2 adaptively without modifying the matching algorithm.

• 전자공학회논문지B
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• 제32B권8호
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• pp.1074-1084
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• 1995

In this paper, we investigate edge extraction and segmentation of range images. We first discuss problems that arise in the conventional region-based segmentation methods and edge-based ones using principal curvatures, then we propose an edge-based algorithm. In the proposed algorithm, we extract edge contours by using the Gaussian filter and directional derivatives, and segment a range image based on extracted edge contours, Also we present the problem that arises in the conventional thresholding, then we propose a new threshold selection method. To solve the problem that local maxima of the first- and second- order derivatives gather near step edges, we first find closed roof edge contours, fill the step edge region, and finally thin edge boundaries. Computer simulations with several range images show that the proposed method yields better performance than the conventional one.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권2호
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• pp.146-159
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• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• 전기학회논문지
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• 제57권11호
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.