• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.670-673
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• 2002

This paper proposes a digital algorithm for gray-scale/color image segmentation of real-time video signals and a cell-network-based implementation architecture in state-of-the-art CMOS technology. Through extrapolation of design and simulation results we predict that about 300$\times$300 pixels can be integrated on a chip at 100nm CMOS technology, realizing very high-speed segmentation at about 1600sec per color image. Consequently real-time color-video segmentation will become possible in near future.

• 디지털산업정보학회논문지
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• 제11권4호
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• pp.89-97
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• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.619-622
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• 1999

This paper describes a new semi-automatic segmentation algorithm based on color information. Semi-automatic segmentation mainly consists of intra-frame segmentation and inter-frame segmentation. While intra-frame segmentation extracts video objects of interest from boundary information provided by the user and intensity information of the image, inter-frame segmentation partitions the image into the video objects and background by tracking the motion of video objects. For inter-frame segmentation, color information (Y, Cb and Cr) of the current frame can be used efficiently in order to find the exact boundary of the video objects. In this paper we propose a new region growing algorithm which can maximize the ability of region differentiation, while preserving features of each color component.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 추계학술발표대회
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• pp.429-430
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• 2011

Roughness set theory is a popular topic to use in color-image segmentation. A new popular color image segmentation algorithm is proposed by scientists with the point using traditional histogram and Histon construct roughness set histogram. But, there is still a problem about that is the correlativity of color vector in roughness set histogram, which take an inactive effect in the process of color-image segmentation. Therefore, this paper represents further research based on this and proposed an improved method proved through lot of experiments. The experimental result reduces the correlativity of color vector in roughness set histogram and calculation time remarkably.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.333-337
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• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.453-456
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• 2003

Compared with other features of the image, color features are less sensitive to noise and background complication. Besides, this adding to object segmentation has more accuracy of image retrieval. This paper presents object segmentation and HAQ(Histogram Analysis and Quantization) algorithm approach to extract features(the object information and the characteristic colors) of an image. The empirical results shows that this method presents exactly spatial and color information of an image as image retrieval's feature.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.114-127
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• 2017

This paper proposes a color image coding algorithm based on shape-adaptive all phase biorthogonal transform (SA-APBT). This algorithm is implemented through four procedures: color space conversion, image segmentation, shape coding, and texture coding. Region-of-interest (ROI) and background area are obtained by image segmentation. Shape coding uses chain code. The texture coding of the ROI is prior to the background area. SA-APBT and uniform quantization are adopted in texture coding. Compared with the color image coding algorithm based on shape-adaptive discrete cosine transform (SA-DCT) at the same bit rates, experimental results on test color images reveal that the objective quality and subjective effects of the reconstructed images using the proposed algorithm are better, especially at low bit rates. Moreover, the complexity of the proposed algorithm is reduced because of uniform quantization.

• Journal of Biosystems Engineering
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• 제33권6호
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• pp.446-452
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• 2008

Machine vision system was used for analyzing leaf color disorders of tomato plants in a greenhouse. From the day when a few leave of tomato plants had started to wither, a series of images were captured by 4 times during 14 days. Among several color image spaces, Saturation frame in HSI color space was adequate to eliminate a background and Hue frame was good to detect infected disease area and tomato fruits. The processed image ($G{\sqcup}b^*$ image) by OR operation between G frame in RGB color space and $b^*$ frame in $La^*b^*$ color space was useful for image segmentation of a plant canopy area. This study calculated a ratio of the infected area to the plant canopy and manually analyzed leaf color disorders through an image segmentation for Hue frame of a tomato plant image. For automatically analyzing plant leave disease, this study selected twenty-seven color patches on the calibration bars as the corresponding to leaf color disorders. These selected color patches could represent 97% of the infected area analyzed by the manual method. Using only ten color patches among twenty-seven ones could represent over 85% of the infected area. This paper showed a proposed machine vision system may be effective for evaluating various leaf color disorders of plants growing in a greenhouse.

• 전자공학회논문지B
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• 제33B권8호
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• pp.68-80
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• 1996

In this paper, we propose a novel color image segmentation algorithm based on clustering in 3-dimensional color space employing the mathematical morphology. More specifically, since we take into account the topological properties such as the shape, connectivity and distribution of clusters in the clustering process, the number of clusters in the color cube, as well as their centers, can be easily obtained, without a priori knowledge on the input images. Intensive computer simulation has been performed and the results are discussed in this paper. The resutls of the simulation on the images in various color coordinates show that the segmentation is independent of the choice of color coordinates and the shape of clustes. Segmentation results of the vector quantizer are also presented for the comparison purpose.

• 한국멀티미디어학회논문지
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• 제7권3호
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• pp.319-326
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• 2004

본 논문에서는 물체의 경계선을 고려한 칼라 영상 분할 방법을 제안한다. 제안 방법은 먼저 원영상을 벡터 양자화한 후 양자화된 영상의 인덱스 맵을 이용하여 초기 영역을 설정하였다. 그 후 HSI컬러 공간을 이용한 영역 병합에서 물체의 경계선을 고려하기 위해 경계선 제한 성분을 적용하여 영역들을 병합하였다. 또한 RGB 컬러 공간을 이용하여 HSI 컬러 공간에서 병합되지 않은 영역들을 병합하였다. 그리 고 영역병합 알고리즘을 통해 반복적인 처리를 감소시킴으로써 처리 시간을 줄였다. 실험 결과에서는 다양한 영상에 대해 주요 영역들의 분할 결과 및 처리소요시간에서 우수한 성능을 보였다.