• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.379-383
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• 2015

This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.325-328
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• 2002

본 논문에서는 사람의 식별과 감정을 인식하기 위한 하나의 방법을 제안한다. 제안된 방법은 색차 정보에 의한 형판의 위치 인식과 형판 벡터 추출에 기반한다. 단일 색차 공간만을 이용할 경우 살색 영역을 정확히 추출하기 힘들다. 이를 보완하기 위해서 여러 가지 색차 공간을 병행하여 살색 영역을 추출하며, 이를 응용하여 각각의 형판을 추출하는 방법을 제안한다. 그리고, 사람의 식별과 감정 인식을 위해서 추출된 형판에 대한 각각의 특징 벡터 추출 방법을 제시하며, 마지막으로 추출된 형판 벡터를 이용하여 신경망을 통한 학습과 인식을 수행하는 방법을 제시한다.

• Journal of the Korean Society of Costume
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• v.44
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• pp.131-141
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• 1999

The purpose of this study was to investigate the various expressions with materials in fashion illustration using CAD comparing with hand work. The design motive for this study is to be selected from Bell Epoque era which was the revolutionary period in fashion illustration. The results of this study were as follows: 1. By using CAD, water color was well expressed to repeat brushing for the clarity, darkness, and brightness. It was more effective to use CAD in layering or duplicating complex and intricate patterns because the base color gets to be concealed under the repeated oil pastel. Acryl, like oil pastel, was easily absorbed in canvas, so it is effective to repeat brushing for expressing pure color. It was inconvenient to use wax crayon for controlling the moderate opacity because wax crayon absorbed water color dye stuffs, so crayon line was concealed when repetitions were being done. 2. The advantage of using CAD was convenience for getting rid of troublesome process and inefficient works. Also, CAD had a good tool like oil pastel in the use of coloring work by using pure color. By using CAD, various expressions on materials and texture of surface can be achieved effectively. Also, it is very strong substitute for time-saving, convenience, economic aspects from providing simple instrument, and production in the state of various kinds of paper and canvas as a method of visualization. Therefore, fashion illustration using CAD provides effective way of producing works, and gives promising vision in the future.

• Agricultural and Biosystems Engineering
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• v.1 no.1
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• pp.22-29
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• 2000

The development of bio-response feedback control system known as the speaking plant approach has been a challenging task for plant production engineers and scientists. In order to achieve the aim of developing such a bio-response feedback control system, the primary concern should be to develop a practical non-invasive technique for monitoring plant growth. Those who are skilled in raising plants can sense whether their plants are under adequate water conditions or not, for example, by merely observing minor color and tone changes before the plants wilt. Consequently, using machine vision, it may be possible to recognize changes in indices that describe plant conditions based on the appearance of growing plants. The interpretation of image information of plants may be based on image features extracted from the original pictorial image. In this study, the performance of a finite element retina was optimized by a genetic algorithm. The optimized finite element retina was evaluated based on the performance of neural plant growth monitor that requires input data given by the finite element retina.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.11a
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• pp.268-270
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• 2014

Object extraction problem is one of the most important topics in the research area of computer vision, this type of technique can be widely used in practical, such as image processing, robot vision, automatically traffic guide and so on. In this paper, we propose a different way to estimate the background and foreground without any previous training procedure, this approach can be used for automatic object extraction in the future. A simple experiment result shows that our approach has a good potential for the further more practical application.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.65-69
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• 2001

Edge detection is the most important process that belongs to the first step in image recognition or vision system and can determine the efficiency valuation. The edge detection with color images is very difficult. because color images have lots of information that contain not only general information representing shape, brightness and so on but also that representing colors. In this paper, we propose architecture of universalized Color Morphological Pyramids(CMP) which is able to give effective edge detection. Image pyramid architecture is a successive image sequence whose area ratio 2$\^$-1/(ι= 1, 2, . . . ,N) after filtering and subsampling of input image. In this technique, noise removed by sequential filtering and resolution is degraded by downsampling using CMP in various color spaces. After that, new level images are constructed that apply formula using distance of neighbor vectors in close level images and detection its image.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.12
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• pp.1259-1266
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• 2014

Road signs provide important safety information about road and traffic conditions to drivers. Road signs include not only common traffic signs but also warning information regarding unexpected obstacles and road constructions. Therefore, accurate detection and identification of road signs is one of the most important research topics related to safe driving. In this paper, we propose a 3-D vision technique to automatically detect and track road signs in a video sequence which is acquired from a stereo vision camera mounted on a vehicle. First, color information is used to initially detect the sign candidates. Second, the SVM (Support Vector Machine) is employed to determine true signs from the candidates. Once a road sign is detected in a video frame, it is continuously tracked from the next frame until it is disappeared. The 2-D position of a detected sign in the next frame is predicted by the 3-D motion of the vehicle. Here, the 3-D vehicle motion is acquired by using the 3-D pose information of the detected sign. Finally, the predicted 2-D position is corrected by template-matching of the scaled template of the detected sign within a window area around the predicted position. Experimental results show that the proposed method can detect and track many types of road signs successfully. Tracking comparisons with two different methods are shown.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1243-1253
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• 1993

Visual features of a mushroom(Lentinus Edodes L) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading looks simple, a decision making undereath the simple action comes form the results of the complex neural processing of the visual image. And processing details involved in the visual recognition of the human brain has not been fully investigated yet. Recently, however, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, a research of the neuro-net based human like information processing toward the agricultural product and processing are widely open and promising. In this pape , neuro-net based grading and sorting system was developed for a mushroom . A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features and their corresponding grades were used as input/output pairs for training the neural network and the trained results of the network were presented . The computer vision system used is composed of the IBM PC compatible 386DX, ITEX PFG frame grabber, B/W CCD camera , VGA color graphic monitor , and image output RGB monitor.

• Food Science of Animal Resources
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• v.40 no.6
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• pp.896-907
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• 2020

Imaging technique or computer vision (CV) technology has received huge attention as a rapid and non-destructive technique throughout the world for measuring quality attributes of agricultural products including meat and meat products. This study was conducted to test the ability of CV technology to predict the quality attributes of beef. Images were captured from longissimus dorsi muscle in beef at 24 h post-mortem. Traits evaluated were color value (L*, a*, b*), pH, drip loss, cooking loss, dry matter, moisture, crude protein, fat, ash, thiobarbituric acid reactive substance (TBARS), peroxide value (POV), free fatty acid (FFA), total coliform count (TCC), total viable count (TVC) and total yeast-mould count (TYMC). Images were analyzed using the Matlab software (R2015a). Different reference values were determined by physicochemical, proximate, biochemical and microbiological test. All determination were done in triplicate and the mean value was reported. Data analysis was carried out using the programme Statgraphics Centurion XVI. Calibration and validation model were fitted using the software Unscrambler X version 9.7. A higher correlation found in a* (r=0.65) and moisture (r=0.56) with 'a*' value obtained from image analysis and the highest calibration and prediction accuracy was found in lightness (r²_c=0.73, r²_p=0.69) in beef. Results of this work show that CV technology may be a useful tool for predicting meat quality traits in the laboratory and meat processing industries.