컬러 영상에서 평균 이동 클러스터링과 단계별 영역 병합을 이용한 자동 원료 분류 알고리즘 Automatic Classification Algorithm for Raw Materials using Mean Shift Clustering and Stepwise Region Merging in Color원문보기
본 논문에서는 카메라로부터 입력된 영상으로부터 쌀, 커피, 녹차 등 다양한 원료를 양품과 불량품으로 자동 분류하기 위한 분류 모델을 제안한다. 현재 농산물 원료 분류를 위해서 주로 숙달된 노동력의 육안 선택에 의존하고 있지만 작업시간이 길어질수록 반복적인 작업에 의해 분류 능력이 현저히 떨어지는 문제점이 있다. 노동력에 부분적으로 의존하는 기존 제품의 문제점을 해결하기 위해, 본 논문에서는 평균-이동 클러스터링 알고리즘과 단계별 영역 병합 알고리즘을 결합하는 비전기반 자동 원료 분류 알고리즘을 제안한다. 우선 입력 원료 영상에서 평균-이동 클러스터링 알고리즘을 적용하여 영상을 N개의 클러스터 영역으로 분할한다. 다음단계에서 N개의 클러스터 영역 중에서 대표 영역을 선택하고 이웃 영역들의 영역의 색상과 위치 근접성을 기반으로 단계별 영역 병합 알고리즘을 적용하여 유사한 클러스터 영역을 병합한다. 병합된 원료 객체는 RG, GB, BR의 2D 색상 분표로 표현되고, 병합된 원료 객체에 대해 색상 분포 타원을 만든다. 이후 미리 실험적으로 설정된 임계값을 적용하여 원료를 양품과 불량품을 구분한다. 다양한 원료 영상에 대해 본 논문에서 제안하는 알고리즘을 적용한 결과 기존의 클러스터링 알고리즘이나 상업용 분류 방법에 비해 사용자의 인위적 조작이 덜 필요하고 분류성능이 우수한 결과를 나타냄을 알 수 있었다.
본 논문에서는 카메라로부터 입력된 영상으로부터 쌀, 커피, 녹차 등 다양한 원료를 양품과 불량품으로 자동 분류하기 위한 분류 모델을 제안한다. 현재 농산물 원료 분류를 위해서 주로 숙달된 노동력의 육안 선택에 의존하고 있지만 작업시간이 길어질수록 반복적인 작업에 의해 분류 능력이 현저히 떨어지는 문제점이 있다. 노동력에 부분적으로 의존하는 기존 제품의 문제점을 해결하기 위해, 본 논문에서는 평균-이동 클러스터링 알고리즘과 단계별 영역 병합 알고리즘을 결합하는 비전기반 자동 원료 분류 알고리즘을 제안한다. 우선 입력 원료 영상에서 평균-이동 클러스터링 알고리즘을 적용하여 영상을 N개의 클러스터 영역으로 분할한다. 다음단계에서 N개의 클러스터 영역 중에서 대표 영역을 선택하고 이웃 영역들의 영역의 색상과 위치 근접성을 기반으로 단계별 영역 병합 알고리즘을 적용하여 유사한 클러스터 영역을 병합한다. 병합된 원료 객체는 RG, GB, BR의 2D 색상 분표로 표현되고, 병합된 원료 객체에 대해 색상 분포 타원을 만든다. 이후 미리 실험적으로 설정된 임계값을 적용하여 원료를 양품과 불량품을 구분한다. 다양한 원료 영상에 대해 본 논문에서 제안하는 알고리즘을 적용한 결과 기존의 클러스터링 알고리즘이나 상업용 분류 방법에 비해 사용자의 인위적 조작이 덜 필요하고 분류성능이 우수한 결과를 나타냄을 알 수 있었다.
In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends ...
In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.
In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.
In this paper, to resolve this problem we proposed an algorithm whereby regions are generated in RGB space, applying the mean-shift clustering algorithm without mapping the raw material image directly to the color space, and regions are merged into significant objects using the stepwise merging algorithm. An algorithm that is robust in the presence of noise and requires no additional work of the user was developed by mapping the result of merged objects to each color space in order to estimate the threshold value ellipse.
대상 데이터
For the experiment, rice and coffee products used most widely for classification of agricultural products and plastic materials used most widely for classification of waste materials were classified. The experimental tests consisted of 14 color raw material images collected from the commercial product of [1], including 62 rices, 14 coffees, and 11 plastics.
성능/효과
In this paper, to resolve this problem we proposed an algorithm whereby regions are generated in RGB space, applying the mean-shift clustering algorithm without mapping the raw material image directly to the color space, and regions are merged into significant objects using the stepwise merging algorithm. An algorithm that is robust in the presence of noise and requires no additional work of the user was developed by mapping the result of merged objects to each color space in order to estimate the threshold value ellipse.
As the performance rate on average of the proposed algorithm was about 84.9 % while that of K-means clustering [4] was 3.47 % and that of the commercial algorithm [1] was 41.4 %, it is clear that the performance of the proposed algorithm is superior to that of the other two algorithms. In particular, the classification rate of K-means clustering was the lowest, which is thought to be because the initial center point value of the K-means clustering algorithm is selected randomly and because the algorithm is sensitive to noise in the clustering process.
5 % on average. It can be seen that, as for overall performance, the classification rate of the proposed method was improved compared to that of the existing commercial products by about 40 % because it segments raw materials into objects and sets the threshold value on the basis of the color distribution model for the segmented objects.
In particular, the classification rate of K-means clustering was the lowest, which is thought to be because the initial center point value of the K-means clustering algorithm is selected randomly and because the algorithm is sensitive to noise in the clustering process. It can be seen that, as the plastic used for the experiment included diverse colors, performance was lower than for other raw materials by 18.5 % on average. It can be seen that, as for overall performance, the classification rate of the proposed method was improved compared to that of the existing commercial products by about 40 % because it segments raw materials into objects and sets the threshold value on the basis of the color distribution model for the segmented objects.
In this paper, we proposed an algorithm that can effectively differentiate good products out of grain, ore, or other raw materials using RGB data from color images. The existing method sets the color threshold value using K-means clustering or other methods, but after mapping raw material to color spaces, such methods encounter the problem of requiring a user to correct the threshold region because, since existing methods are sensitive to noise, an accurate ellipse cannot be generated.
후속연구
In future studies, we intend to widen the scope of application to the classification of ores in addition to that of grains or waste materials, and to design a system with a fast classification rate using smaller memory by improving classification speed.
K. SangJun, J. JiHyun, and K, Byoung Chul "Automatic Source Classification Algorithm using Mean-shift Clustering and stepwise merging in Color Image," The 2015 Fall Conference of the KIPS, pp. 1597 -1599, 2015.
C. Dorin and M. Peter, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on pattern Analysis and Machine Intelligence, vol.24, no.5, pp.603-619, May. 2002.
E. Nick, in Digital Image Processing: A Practical Introduction using Java, Addison-Wesley, 2000.
K. Tapas, M. M. Davidt, N. Nathan, P. Christine, S. Ruth, and W. Y. Angela, "A local search approximation algorithm for k-means clustering," 18th ACM Symposium on Computational Geometry, pp. 10-18, 2002.
B. C. Ko, J. Gim, and J. Y. Nam, “Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake,” Micron, vol. 42, pp. 695–705, 2011.
J. K. Anil, in Fundamentals of digital image processing, Prentice hall, 1989.
M. Jeong, B. C. Ko, and J. Y. Nam, "Overlapping Nuclei Segmentation based on Bayesian Networks and Stepwise Merging Strategy," Journal of Microscopy, vol. 235, pp. 188–198, 2009.
B. C. Ko and J. Y. Nam, “Object-of-Interest Image Segmentation using Human Attention and Semantic Region Clustering,” Journal of Optical Society of America A: Optics, Image Science, and Vision, vol. 23, no. 10, pp. 2462-2470, 2006.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.