• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.1677-1683
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• 2006

Color image segmentation is useful for fast retrieval in large image database. For that purpose, new image segmentation technique based on the probability of pixel distribution in the image is proposed. Color image is first divided into R, G, and B channel images. Then, pixel distribution from each of channel image is extracted to select to which it is similar among the well known probabilistic distribution function-Weibull, Exponential, Beta, Gamma, Normal, and Uniform. We use sum of least square error to measure of the quality how well an image is fitted to distribution. That P.d.f has minimum score in relation to sum of square error is chosen. Next, each image is quantized into 4 gray levels by applying thresholds to the c.d.f of the selected distribution of each channel. Finally, three quantized images are combined into one color image to obtain final segmentation result. To show the validity of the proposed method, experiments on some images are performed.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.39-42
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• 2003

In this paper, we propose a background segmentation method in low resolution color image. A segmentation algorithm is based on color and edge information. In edge image, adaptive and local thresholds are applied to suppress paint boundaries. Through our experiments, the proposed algorithm efficiently segments background from objects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권7호
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• pp.2400-2419
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• 2014

Pre-segmented pixels can reduce the difficulty of segmentation and promote the segmentation performance. This paper proposes a novel segmentation method based on merging texture superpixels by computing inner similarity. Firstly, we design a set of Gabor filters to compute the amplitude responses of original image and compute the texture map by a salience model. Secondly, we employ the simple clustering to extract superpixles by affinity of color, coordinates and texture map. Then, we design a normalized histograms descriptor for superpixels integrated color and texture information of inner pixels. To obtain the final segmentation result, all adjacent superpixels are merged by the homogeneity comparison of normalized color-texture features until the stop criteria is satisfied. The experiments are conducted on natural scene images and synthesis texture images demonstrate that the proposed segmentation algorithm can achieve ideal segmentation on complex texture regions.

• 전자공학회논문지CI
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• 제45권1호
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• pp.11-24
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• 2008

효율적인 저차원의 인덱싱을 제공하기 위해 이미지를 유사한 성질을 갖는 영역으로 나누고, 나누어진 영역에 대해 유사성을 비교하는 영역 기반 이미지 검색이 제안되었다. 그러나 영역 기반 이미지 검색은 이미지를 유사한 영역으로 나누기 위한 이미지 세그멘테이션 기술이 추가적으로 필요하다. 일반적인 칼라 자연 이미지의 경우 다양한 칼라와 질감 성분을 갖는 영역으로 나누는 것은 많은 어려움이 있다. 본 논문에서는 자동적인 칼라 이미지 세그멘테이션 알고리즘을 제안한다. 제안하는 세그멘테이션 방법은 양자화를 통해 칼라수를 줄이고 양자화 된 이미지를 Fisher의 클래스 선형 판별식을 이용하여 이미지의 전체적인 에지를 보여주는 그레이 레벨 이미지를 생성한다. 이렇게 얻은 그레이 레벨 에지 이미지를 지역적 임계치 비교를 통해 이진 에지 이미지로 변환하고 이진 에지의 끊어진 부분을 찾아내어 인접 에지에 연결하여 영역을 생성한다. 마지막으로 나누어진 영역간의 유사성을 비교하고 유사한 영역을 병합하여 최종 세그멘테이션 결과 이미지를 생성한다. 본 논문에서는 세그멘테이션 알고리즘을 이용한 영역 기반 이미지 검색 시스템을 구현하였으며, 다양한 실험에 의하면 제안한 세그멘테이션 방법이 다양한 이미지에 대하여 양질의 세그멘테이션 결과를 보이는 것으로 나타났다.

• 반도체디스플레이기술학회지
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• 제13권4호
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• pp.49-54
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• 2014

This paper proposes a new color segmentation method of vehicle license plates in the RGB color space. Firstly, the proposed method shifts the histogram of an input image rightwards and then stretches the image of the histogram slide. Secondly, the method separates each of the three RGB color components and performs the adaptive threshold processing with the three components, respectively. Finally, it combines the three components under the condition of making up a segment color and removes noises with the morphological processing. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using real vehicle images. The results show that the proposed algorithm is successful for most vehicle images. However, the method fails in some vehicles when the body and the license plate have the same color.

• KIEE International Transaction on Systems and Control
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• 제12D권1호
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• pp.1-6
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• 2002

In this study, we propose the new color-based image retrieval technique using the representative colors of images and their ratios to a total image size obtained through color segmentation in HSV color space. Color information of an image is described by reconstructing the color histogram of an image through Gaussian modelling to its representative colors and ratios. And the similarity between two images is measured by histogram intersection. The proposed method is compared with the existing methods by performing retrieval experiments for various 1280 trademark image database.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권6호
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• pp.523-531
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• 2005

확산(Diffusion)을 이용한 기존의 칼라영상 분할은 확산의 횟수가 반복될수록 경계선 정보가 적절히 유지되지 못하거나 잡음을 제거하지 못함으로써 워터쉐드(Watershed) 알고리즘을 적용하는 경우, 과분할을 피할 수 없다는 단점을 갖고 있다. 본 논문에서는 수리 형태학(Mathematical Morphology)과 비선형 확산(Non-Linear Diffusion)을 함께 적용하여 과분할의 문제점을 제거한 워터쉐드 결과를 얻을 수 있는 칼라영상 분할방법을 제안한다. 임의의 칼라 영상을 LUV 색상공간으로 변환하여, 그 각각의 색상공간에 수리 형태학을 응용한 재구성에 의한 닫힘(Reconstruction) 연산과 비선형 확산을 함께 적용하여 경계선을 적절히 유지하면서 잡음을 제거한 단순 영상을 획득할 수 있다. 이 영상에서 칼라 영상의 기울기(Gradient) 정보를 획득하고, 워터쉐드 알고리즘을 적용하여 영상을 분할한다. 실험 결과, 기존의 방법보다 과분할이 현저히 제거되고, 칼라 영상이 매우 효과적으로 분할됨을 확인하였다

• 전기학회논문지
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• 제59권6호
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• pp.1136-1142
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• 2010

In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.

• 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.