• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.91-101
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• 2013

This paper proposes a new re-coloring method for the people with the red-green color vision deficiency (CVD). These people have difficulty in discriminating the red and green colors since they abnormally perceive the hue and luminance value of the colors. We introduce a color transformation that adjusts the hue and luminance value in HSV color space. The color transformation is determined according to the severity of CVD. Our aim is to maintain the color differences in original image while maintaining the recolored image to be natural to the people with normal color vision. Experimental results show that the proposed method can yield more comprehensible images for the people with red-green CVD while maintaining the naturalness of the recolored images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.273-275
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• 2011

본 논문에서는 허프변환 및 HSV 색변환을 이용한 효율적인 차선검출의 최적화 알고리즘을 제안한다. 차선 검출의 고속화를 위해 차선과 카메라의 위치를 감안하여 고정된 관심영역(ROI_LB)을 정하고 검출 영역을 감소시킨다. 정해진 관심영역 내에서 허프변환을 적용해 차선을 검출하고 이를 위해 Sobel Mask와 Threshold를 사용한다. 또한, HSV 색 공간을 이용하여 황색 선과 백색 선을 구별해내며 차선 이동 시에 "MOVEMENT"이라는 문자열을, 중앙선을 넘어가면 "DANGEROUS"이라는 문자열을 출력한다. 제안하는 방법의 실험 결과는 복잡한 도로 동영상에서 효과적으로 차선을 인식하고 색 구별을 하였으며 제안 방법의 유효성을 검증하기 위해 다양한 실제 차선 패턴을 대상으로 한 실험결과를 제시한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.165-173
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• 2015

People with color vision deficiency (CVD) experience difficulties in discriminating some color combinations and color differences due to the abnormal retinal cone systems. While there exist smartphones with a re-coloring function for CVD, monitors do not provide the re-coloring function. In this paper, we propose a new re-coloring algorithm that adjusts the displayed colors for CVD using a color controller embedded in the monitor. The proposed algorithm converts the hue and saturation in HSV color space, according to the type and strength of the color deficiency. The results of the performance evaluation with a certain number of people with CVD show that the proposed system can convert colors imperceptible into perceptible.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.20-27
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• 1998

This paper describes color clustering method based on color difference in the uniform Munsell color space obtained from hue, saturation, and value. The proposed method operates in the uniform HSV color space which is approximated using ${L^*}{a^*}{b^*}$ coordinate system based on the RGB inputs. A clustering and color difference evaluation are proposed by thresholding NBS unit which is likely to Balinkin color difference equation. Region segmentation and isolation process are carried out ISO DATA algorithm which is a self iterative clustering technique. Through the clustering of 2 input images according to the threshold value, satisfactory results are obtained. So, in conclusion, it is possible to extract result of better region segmentation using human color perception of the objects.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.270-274
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• 2015

We suggest that HSV Color Space may be used to detect a vehicle detecting system at nighttime. It is essential that a licence plate should be extracted when a vehicle is under surveillance. To do so, a licence plate may be enlarged to certain size after the aimed vehicle is taken picture from a distance by using Pan-Tilt-Zoom Camera. Either Mean-Shift or Optical Flow Algorithm is generally used for the purpose of a vehicle detection and trace, even though those algorithms have tendency to have difficulty in detection and trace a vehicle at night. By utilizing the fact that a headlight or taillight of a vehicle stands out when an input image is converted in to HSV Color Space, we are able to achieve improvement on those algorithms for the vehicle detection and trace. In this paper, we have shown that at night, the suggested method is efficient enough to detect a vehicle 93.9% from the front and 97.7% from the back.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.550-552
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• 1998

스포츠 비디오는 역동적인 특성과 비정형적인 구조를 가지고 있으므로 뉴스와 같은 정형적인 비디오와는 달리 분석이 쉽지 않다. 본 논문에서는 이러한 어려움을 극복하기 위하여 축구 경기에서 하이라이트를 추출하여 색인하고 이에 대하여 선수 위치 추적, 파노라마 영상 구성, 경기장 모델 상에서의 선수 이동 궤적 도시 등을 수행하는 방법을 제안한다. 이를 위하여 제한된 색상의 HSV 영상을 구성하여 골대와 선수 위치를 추적하고, 움직임 벡터를 추출하여 카메라 동작을 분석하였으며 경기장 모델 구성을 위해 경기장 내의 특징점을 추출하여 투영 변환을 수행하였다. 실험 결과를 통해서 제안된 방법이 축구 경기 비디오 분석에 효율적으로 이용될 수 있음을 확인할 수 있다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1054-1062
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• 2022

The population of Korea's solar salt-producing regions is rapidly aging, resulting in a decrease in the number of productive workers. In solar salt production, salt collection is the most labor-intensive operation because existing salt collection vehicles require human operators. Therefore, we intend to develop an unmanned solar salt collection vehicle to reduce manpower requirements. The unmanned solar salt collection vehicle is designed to identify the salt collection status and location in the salt plate via color detection, the color detection performance is a crucial consideration. Therefore, an image processing algorithm was developed to improve color detection performance. The algorithm generates an around-view image by using resizing, rotation, and perspective transformation of the input image, set the RoI to transform only the corresponding area to the HSV color model, and detects the color area through an AND operation. The detected color area was expanded and noise removed using morphological operations, and the area of the detection region was calculated using contour and image moment. The calculated area is compared with the set area to determine the location case of the collection vehicle within the salt plate. The performance was evaluated by comparing the calculated area of the final detected color to which the algorithm was applied and the area of the detected color in each step of the algorithm. It was confirmed that the color detection performance is improved by at least 25-99% for salt detection, at least 44-68% for red color, and an average of 7% for blue and an average of 15% for green. The proposed approach is well-suited to the operation of unmanned solar salt collection vehicles.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.459-465
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• 2013

This paper is a study on an object extraction method which using color features of an object and background in the image. A human recognizes an object through the color difference of object and background in the image. So we must to emphasize the color's difference that apply to extraction result in this image. Therefore, we have converted to HSV color images which similar to human visual system from original RGB images, and have created two each other images that applied Median Filter and we merged two Median filtered images. And we have applied the Mean Shift algorithm which a data clustering method for clustering color features. Finally, we have normalized 3 image channels to 1 image channel for binarization process. And we have created object map through the binarization which using average value of whole pixels as a threshold. Then, have extracted major object from original image use that object map.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.679-686
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• 2022

In this paper, we implemented an algae removal device that recognizes and removes algae existing in water using Raspberry Pi camera and DO (Dissolved Oxygen) sensor. The Raspberry Pi board recognizes the color of green algae by converting the RGB values obtained from the camera into HSV. Through this, the location of the algae is identified and when the amount of dissolved oxygen's decrease at the location is more than the reference value using the DO sensor, the algae removal device is driven to spray the algae removal solution. Raspberry Pi's camera uses OpenCV, and the motor movement is controlled according to the output value of the DO sensor and the result of the camera's green algae recognition. Algae recognition and spraying of algae removal solution were implemented through Arduino and Raspberry Pi, and the feasibility of the proposed portable algae removal device was verified through experiments.