• Journal of the Optical Society of Korea
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• v.7 no.4
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• pp.234-239
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• 2003

Objective methods to assess quantitatively port wine stain (PWS) blanching in response to laser therapy are needed to improve laser therapeutic outcome. Previous studies have attempted to assess objectively PWS color based on point measurement devices. To date, these approaches have typically been limited by a number of factors such as small test area and need for contact. To address these issues, a polarization spectral imaging system and an image analysis method have been developed to evaluate quantitatively erythema and melanin content distribution in skin. The developed polarization spectral imaging system minimizes artifacts such as glaring, shadowing, and non-uniform illumination that interfere with image fidelity. Furthermore, the image analysis method has been employed to get images of skin melanin and erythema indices from the acquired color images for quantitative analysis. Finally, using PWS patient color image, the effectiveness in laser treatment of PWS was evaluated by calculating relative erythema index image that is the relative erythema index of PWS region to the normal region. The developed device and analysis method appears to be a simple and effective method for quantitative analysis of PWS blanching.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.828-831
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• 2005

In this paper we propose a method to detect human faces from color image using pupil-template matching. Face detection is done by three stages. (i)separating skin regions from non-skin regions; (ii)generating a face regions by application of the best-fit ellipse; (iii)detecting face by pupil-template. Detecting skin regions is based on a skin color model. we generate a gray scale image from original image by the skin model. The gray scale image is segmented to separated skin regions from non-skin regions. Face region is generated by application of the best-fit ellipse is computed on the base of moments. Generated face regions are matched by pupil-template. And we detection face.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.311-321
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• 2007

For fast and robust face detection, this paper proposes an approach for face detection using fusion of heterogeneous template matching. First, we detect skin regions using a model of skin color which covers various illumination and races. After reducing a search space by region labelling and filtering, we apply template matching with skin color and edge to the detected regions. Finally, we detect a face by finding the best choice of template fusion. Experimental results show the proposed approach is more robust in skin color-like environments than with a single template matching and is fast by reducing a search space to face candidate regions. Also, using a global accumulator can reduce excessive space requirements of template matching.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2727-2732
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• 2014

Recently, the researches of object tracking and recognition using Microsoft's Kinect are being actively studied. In this environment human hand detection and tracking is the most basic technique for human computer interaction. This paper proposes a method of improving the accuracy of the detected hand region's boundary in the cluttered background. To do this, we combine the hand detection results using the skin color with the extracted depth image from Kinect. From the experimental results, we show that the proposed method increase the accuracy of the hand region detection than the method of detecting a hand region with a depth image only. If the proposed method is applied to the sign language or gesture recognition system it is expected to contribute much to accuracy improvement.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.25-30
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• 2008

In this paper, we propose a color quantization method regarding facial image for mobile services. For facial images, skin colors are more emphasized. First, we extract skin-color mask in the image and divide the image into two regions. Next, we extract color pallette for two regions respectively. In the proposed method, the loss in the face region is minimized and it can be useful for mobile services considering facial images. From the 8-bit color quantization experiment, we show that the proposed method works well.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.77-85
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• 2014

Usually, the skin pigmentation detection and diagnosis are made by clinicians. In this process it is subjective and non-quantitative. We develop an approach to detect and measure the different pigmentation lesions base on computer vision technology. In the paper we study several usually used skin-detecting color space like HSV, YCbCr and normalized RGB. We compare their performance with illumination influence for detecting the pigmentation lesions better. Base on a relatively stable color space, we propose an approach which is RGB channels vector difference characteristic for the detection. After the object region detection, we also use the difference to measure the difference between the lesion and the surrounding normal skin. From the experiment results, our approach can effectively detect the pigmentation lesion, and perform robustness with different illumination.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.291-298
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• 2009

In this paper we proposed a vision based realtime hand gesture recognition method. To extract skin color, we translate RGB color space into YCbCr color space and use CbCr color for the final extraction. To find the center of extracted hand region we apply practical center point extraction algorithm. We use Kalman filter to tracking hand region and use HMM(Hidden Markov Model) algorithm (learning 6 type of hand gesture image) to recognize it. We demonstrated the effectiveness of our algorithm by some experiments.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.127-132
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• 2011

In this paper, we propose a novel method of adult image classification by combining skin color regions and edges in an input image. The proposed method consists of four steps. In the first step, initial skin color regions are detected by logical AND operation of all skin color regions detected by the existing methods of skin color detection. In the second step, a skin color probability map is created by modeling the distribution of skin color in the initial regions. Then, a binary image is generated by using threshold value from the skin color probability map. In the third step, after using the binary image and edge information, we detect final skin color regions using a region growing method. In the final step, adult image classification is performed by support vector machine(SVM). To this end, a feature vector is extracted by combining the final skin color regions and neighboring edges of them. As experimental results, the proposed method improves performance of the adult image classification by 9.6%, compared to the existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.351-354
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• 2011

This paper discusses on facial features extraction based on proposed skin color model. Different parts of face from input image are segmented based on skin color model. Moreover, this paper also discusses on concept to detect the eye and mouth position on face. A height and width ratio (${\delta}=1.1618$) based technique is also proposed to accurate detection of face region from the segmented image. Finally, we have cropped the desired part of the face. This exactly exacted face part is useful for face recognition and detection, facial feature analysis and expression analysis. Experimental results of propose method shows that the proposed method is robust and accurate.