• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.90-98
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• 2011

The recognition and classification of paddy rice disease are of major importance to the technical and economical aspect of agricultural industry over the world. Computer vision techniques are used to diagnose rice diseases and to efficiently manage crops. Segmentation of lesions is the most important technique to detect paddy rice disease early and accurately. A new Gaussian Mean (GM) method was proposed to segment paddy rice diseases in various color spaces. Different color spaces produced different results in segmenting paddy diseases. Thus, this empirical study was conducted with the motivation to determine which color space is best for segmentation of rice disease. It included five color spaces; NTSC, CIE, YCbCr, HSV and the normalized RGB(NRGB). The results showed that YCbCr was the best color space for optimal segmentation of the disease lesions with 98.0% of accuracy. Furthermore, the proposed method demonstrated that diseases lesions of paddy rice can be segmented automatically and robustly.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.67-74
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• 2001

In this paper, we propose a face detection algorithm using the color and shape information in color still images. The proposed algorithm is only applied to chrominance components(Cb and Cr) in order to reduce the variations of lighting condition in YCbCr color space. Input image is segmented by pixels with skin-tone color and then the segmented mage follows the morphological filtering an geometric correction to eliminate noise and simplify the segmented regions in facial candidate regions. Multiple facial regions in input images can be isolated by connected component labeling. Moreover tilting facial regions can be detected by extraction of second moment-based ellipse features.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.527-539
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• 2010

Most video coding systems use YCbCr color space for their inputs, but RGB space is more preferred in the field of high fidelity video because the compression gain from YCbCr becomes disappeared in the high quality operation region. In order to improve the coding performance of RGB video signal, this paper presents an adaptive frequency-selective weighted prediction algorithm. Based on the sign agreement and the strength of frequency-domain correlation of residual color planes, the proposed scheme adaptively selects the frequency elements as well as the corresponding prediction weights for better utilization of inter-plane correlation of RGB signal. Experimental results showed that the proposed algorithm improves the coding gain of around 13% bitrate reduction, on average, compared to the common mode of 4:4:4 video coding in the state-of-the-art video compression standard, H.264/AVC.

• 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.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.1-12
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• 2009

The correct detection of skin color is an important preliminary process in fields of face detection and human motion analysis. It is generally performed by three steps: transforming the pixel color to a non-RGB color space, dropping the illuminance component of skin color, and classifying the pixels by the skin color distribution model. Skin detection depends on by various factors such as color space, presence of the illumination, skin modeling method. In this paper we propose a 3d skin color model that can segment pixels with several ethnic skin color from images with various illumination condition and complicated backgrounds. This proposed skin color model are formed with each components(Y, Cb, Cr) which transform pixel color to YCbCr color space. In order to segment the skin color of several ethnic groups together, we first create the skin color model of each ethnic group, and then merge the skin color model using its skin color probability. Further, proposed model makes several steps of skin color areas that can help to classify proper skin color areas using small training data.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.131-134
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• 2009

The red-eye effect in photography occurs when using a photographic flash very close to the camera lens, in ambient low light due to in experience. Once occurred, the photographer needs to remove it with image tool that requires time consuming, skillful process. In this paper, we propose a new method to extract and remove such red-eye area automatically. Our method starts with transforming ROB space to YCbCr and HSI space and it extracts the face area by using skin color information. The target red-eye area is then extracted by applying 8-direction contour tracking algorithm and removed. The experiment shows our method's effectiveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.2
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• pp.120-126
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• 2016

In this study, we propose a method for effectively detecting and recognizing the face in image using RBFNNs pattern classifier and HCbCr-based skin color feature. Skin color detection is computationally rapid and is robust to pattern variation for face detection, however, the objects with similar colors can be mistakenly detected as face. Thus, in order to enhance the accuracy of the skin detection, we take into consideration the combination of the H and CbCr components jointly obtained from both HSI and YCbCr color space. Then, the exact location of the face is found from the candidate region of skin color by detecting the eyes through the Haar-like feature. Finally, the face recognition is performed by using the proposed FCM-based RBFNNs pattern classifier. We show the results as well as computer simulation experiments carried out by using the image database of Cambridge ICPR.

• Korean Journal of Computational Design and Engineering
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• v.19 no.2
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• pp.111-118
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• 2014

In tangible augmented reality (AR) environments, the user interacts with virtual objects by manipulating their physical counterparts, but he or she often encounters awkward situations in which his or her hands are occluded by the augmented virtual objects, which causes great difficulty in figuring out hand positions, and reduces both immersion and ease of interaction. To solve the problem of such hand occlusion, skin color information has been usefully exploited. In this paper, we propose an approach to simple and effective construction of a skin color map which is suitable for hand segmentation and tangible AR interaction. The basic idea used herein is to obtain hand images used in a target AR environment by simple image subtraction and to represent their color information by a convex polygonal map in the YCbCr color space. We experimentally found that the convex polygonal map is more accurate in representing skin color than a conventional rectangular map. After implementing a solution for resolving hand occlusion using the proposed skin color map construction, we showed its usefulness by applying it to virtual design evaluation of digital handheld products in a tangible AR environment.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.610-622
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• 2008

Moving object segmentation is an essential technique for various video surveillance applications. The result of moving object segmentation often contains shadow regions caused by the color difference of shadow pixels. Hence, moving object segmentation is usually followed by a shadow elimination process to remove the false detection results. The common assumption adopted in previous works is that, under the illumination variation, the value of chromaticity components are preserved while the value of intensity component is changed. Hence, color transforms which separates luminance component and chromaticity component are usually utilized to remove shadow pixels. In this paper, various color spaces (YCbCr, HSI, normalized rgb, Yxy, Lab, c1c2c3) are examined to find the most appropriate color space for shadow elimination. So far, there have been some research efforts to compare the influence of various color spaces for shadow elimination. However, previous efforts are somewhat insufficient to compare the color distortions under illumination change in diverse color spaces, since they used a specific shadow elimination scheme or different thresholds for different color spaces. In this paper, to relieve the limitations of previous works, (1) the amount of gradients in shadow boundaries drawn to uniform colored regions are examined only for chromaticity components to compare the color distortion under illumination change and (2) the accuracy of background subtraction are analyzed via RoC curves to compare different color spaces without the problem of threshold level selection. Through experiments on real video sequences, YCbCr and normalized rgb color spaces showed good results for shadow elimination among various color spaces used for the experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.163-165
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• 2017

본 논문에서는 카메라 잡음 제거에 딥 러닝 알고리즘을 적용하는 연구를 진행하였다. 합성된 가우시언 잡음에 대하여 좋은 잡음 제거 성능을 보이는 DnCNN(Denoising Convolutional Network)를 이용하여 카메라 잡음을 제거하는 학습과 실험을 진행하였으며, 기준 실험으로는 RGB 색공간의 3채널 모두에 대하여 학습한 신경망(Neural Network)을 사용하였고, 본 논문의 실험에서는 그레이 이미지에 대하여 학습한 신경망을 사용하였다. 신경망의 평가를 위하여 딥 러닝 알고리즘 입력 이미지를 RGB 색공간(RGB Color Space)과 YCbCr 색공간(YCbCr Color Space) 2가지 색공간으로 표현하여 사용하였고, 입력 이미지에 노이즈를 첨가하기 위해 가우시안 노이즈(Gaussian Noise)를 이용하였다. 또한 가우시안 잡음과 다른 성질을 갖는 실제 카메라 잡음에 대해서도 학습과 테스트를 진행하였다.