• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.677-680
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• 2003

Perceptual landmarks are an effective solution for a mobile robot realizing steady and reliable long distance navigation. But the prerequisite is those landmarks must be detected and recognized robustly at a higher speed under various lighting conditions. This made image processing more complicated so that its speed and reliability can not be both satisfied at the same time. Color based target detection technique can separate target color regions from non-target color regions in an image with a faster speed, and better results were obtained only under good lighting conditions. Moreover, in the case that there are other things with a target color, we have to consider other target features to tell apart the target from them. Such thing always happens when we detect a target with its single character. On the other hand, we can generally search for only one target for each time so that we can not make use of landmarks efficiently, especially when we want to make more landmarks work together. In this paper, by making use of the moment invariants of each landmark, we can not only search specified target from separated color region but also find multi-target at the same time if necessary. This made the finite landmarks carry on more functions. Because moment invariants were easily used with some low level image processing techniques, such as color based target detection and gradient runs based target detection etc, and moment invariants are more reliable features of each target, the ratio of target detection were improved. Some necessary experiments were carried on to verify its robustness and efficiency of this method.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.146-149
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• 2009

Face detection and recognition in real-time video constitutes one of the recent topics in the field of computer vision. In this paper, we propose face detection and tracking algorithm using the skin color and haar-like feature in real-time video sequence. The proposed algorithm further includes color space to enhance the result using haar-like feature and skin color. Experiment results reveal the real-time video processing speed and improvement in the rate of tracking.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.594-600
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• 2003

In this paper, we propose a detection method for facial features in color images with various backgrounds and face poses. To begin with, the proposed method extracts face candidacy region from images with various backgrounds, which have skin-tone color and complex objects, via the color and edge information of face. And then, by using the elliptical shape property of face, we correct a rotation, scale, and tilt of face region caused by various poses of head. Finally, we verify the face using features of face and detect facial features. In our experimental results, it is shown that accuracy of detection is high and the proposed method can be used in pose-invariant face recognition system effectively

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.987-990
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• 2005

An important issue in the field of face recognitions and man-machine interfaces is an automatic detection of faces in visual scenes. it should be computationally fast enough to allow an online detection. In this paper we describe our ongoing work on face detection that models the face appearance by edge orientation and color distribution. We show that edge orientation is a powerful feature to describe objects like faces. We present a method for face region detection using edge orientation and a method for face feature detection using local color information. We demonstrate the capability of our detection method on an image database of 1877 images taken from more than 700 people. The variations in head size, lighting and background are considerable, and all images are taken using low-end cameras. Experimental results show that the proposed scheme achieves 94% detection rate with a resonable amount of computation time.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.248-253
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• 2008

In this paper, a novel vehicle detection algorithm is proposed that utilizes the color histogram of the image. The color histogram is used to search the image for regions with shadow, block symmetry, and block non-homogeneity, thereby detecting the vehicle region. First, an integral histogram of the input image is computed to decrease the amount of required computation time for the block color histograms. Then, shadow detection is performed and the block symmetry and block non-homogeneity are checked in a cascade manner to detect the vehicle in the image. Finally, the proposed scheme is applied to both still images taken in a parking lot and an on-road video sequence to demonstrate its effectiveness.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.2
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• pp.116-120
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• 2006

This paper discusses an approach for detecting a new edge in color images. The color image is to be represented by a vector field, and the color image edges are detected as differences in the local vector statistics. This method is based on the calculation for the vector angle between two adjacent pixels. Unlike Euclidean distance in RGB space, the vector angle distinguishes the differences in chromaticity, independent of luminance or intensity. The proposed approach can easily accommodate concepts, such as variable template edge detection, as well as the latest developments in vector order statistics for color image processing. In this paper, it is used not a conventional fixed template operator but a variable template operator The variable template is implemented and experimental results for digital color images are included.

• Journal of IKEEE
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• v.10 no.1 s.18
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• pp.87-95
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• 2006

This paper presents the analyses of the effect of inserting border lines between different color regions on edge detection in color codes, and is not intended to present any new algorithm for color-code recognition. With its role to complement the RFID (radio frequency identification) and the wide and fast spread of digital cameras, an interest on color codes is fast increasing. However, the severe distortion of colors in obtained images prohibits color codes from expanding their applications. To reduce the effect of color distortion it is desirable to process the whole pixels in each color region statistically, instead of relying on some pixels sampled from the region. This requires segmentation, and the segmentation usually requires edge detection. To help detect edges not disconnected, we inserted border lines of the width of two pixels between adjacent color regions. Two colors were used for the border lines: one consisting of white pixels, and the other black pixels. The edge detection was performed on images with either of the two kinds of border lines inserted, and the results were compared to results without inserted border lines. We found that inserting black border lines degraded edge detection by causing zipper effect while inserting white border lines improved it compared to the cases without inserted border lines.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.119-129
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• 2006

Art psychotherapy is widely accepted as an effective tool for diagnosis and treatment of psychological disorders. Important factors for art psychotherapy diagnosis, based on the projection theory that the world of the inner mind appears in drawings, include main color and imbalance of a drawing. This paper develops a system for a machine to determine the main color and the imbalance of a drawing by color recognition and edge detection. Our proposed color recognition procedure adopts NBS(National Bureau of Standards) distance between colors in HVC(Hue, Value, Chroma) color space which is most similar to the human eye's color perception. Our edge detection procedure applies blurring, clustering and transformation to a standard color in a series. Our system considers the numbers of pixels and clusters for each color as a criterion for main color and the frequency of edge coordinates for each region for imbalance. The proposed machine procedure, verified through case studies, can help overcome the subjectivity, ambiguity and uncertainty in human decision involved in art psychotherapy.