• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.127-130
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• 2007

Skin detection research is important role in the 3G of mobile phone for video telephony and security system by using face recognition. We propose skin detection algorithm as preprocessing to the face recognition, and use YCbCr color space. In existing skin detection algorithm using CbCr, skin colors that is brightened by camera flash or sunlight at outdoor in images doesn't acknowledged the skin region. In order to detect skin region accuracy into any circumstance, this paper proposes 3-channel skin detection algorithm.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.32-37
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• 2016

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2011-2025
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• 2021

In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.847-848
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• 2008

In this paper, we proposed a novel edge-oriented color interpolation method which determine the edge direction with hard-decision based on high correlation between different channels. The novel edge direction estimation criterion improves the color interpolation method especially on edges by considering high frequencies between channels.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.6
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• pp.1398-1415
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• 1996

Instead of using three charge-coupled devices (CCDs) for the corresponding color channels, most consumer's most consummer's color macmorders reconstruct color images by using only one CCD with a color filter array (CFA), which periodically samples different color signals. By this reson the resulting image cannot produce the full resolution of the input image. More sepecifically, a single-CCD color camcorder reconstructs red, greed, and blue color channels from a color filter array followed by a CCD. During the reconstruction process, color cross-talk among channels (interchannel distortion) and eriodically space-verying blur (intrachannel distortion) occur. The proposed restoration system reduces distortions due to interchannel interference, and then restores each color channel by removing the corresponding intrachannel distortion. Experimental results show that the proposedsystem provides the improved image in oth objective and subjective senses. A major advantage of the proposed system is feasible to real-time image improvement because it can be implemented by a finite impulse response (FIR) filter structure.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.3
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• pp.80-84
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• 2014

Dark channel prior means that, for undistorted outdoor images, at least one color channel of a pixel or its neighbors have values close to 0, and thus the prior can be used to estimate the amount of distortion for given distorted images. In other words, if an image is distorted by blur, its dark channel values are averaged with neighbor pixel values and thus increase. This paper proposes a method that estimates blur strengths by analyzing the variation of dark channel values caused by blur. Through experiments with images distorted by Gaussian and horizontal motion blur with given strengths, the usefulness of the proposed method is verified.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1481-1490
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• 2017

This paper presents an image quality enhancement algorithm for dark image acquired under poor lighting condition. Various retinex algorithms which are human perception-based image processing methods were proposed to solve this problem. Although MSR(Multi-Scale Retinex) among these algorithm works well under most lighting condition, it shows color degradation because their separate nonlinear processing of RGB color channels. To compensate for the loss of the color, MSRCR(Multi-Scale Retinex with Color Restoration) was proposed. However, it requires high computational load and has additional parameters that need to be adjusted according to input image. In order to overcome this problem, a new retinex algorithm based on MSR is proposed in this paper. The proposed method consists of V channel MSR, saturation correction, and separate contrast enhancement process. Experimental results show that the subjective and objective image quality of the proposed method better than those of the conventional methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.634-638
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• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.92-100
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• 2006

In imaging devices such as digital cameras using a single image sensor, captured images are the sub-sampled images comprised of the pixels that have only one of the three primary colors per a pixel. This images should be restored to the color images through an image processing referred as color interpolation. In this paper, we derive relation between the average of the data from CFA image sensor and the average of each color channel data. By using this relation, a new efficient method for color interpolation is proposed. Also, in order to reduce the zipper effect in a restored image, missing luminance values are interpolated along any edges in the captured image. On the other hand, for the chrominance channel interpolation, we average difference between a chrominance value and a luminance value in a local area, and this average value is added to the pixel value of the interpolated location. The proposed method has been compared with several previous methods, and our experimental results show the better results than the other methods.

• Journal of Advanced Navigation Technology
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• v.14 no.4
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• pp.553-561
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• 2010

This paper proposed the face tracking method which can be effectively applied to the robot's vision system. The proposed algorithm tracks the facial areas after detecting the area of video motion. Movement detection of video images is done by using median filter and erosion and dilation operation as a method for removing noise, after getting the different images using two continual frames. To extract the skin color from the moving area, the color information of sample images is used. The skin color region and the background area are separated by evaluating the similarity by generating membership functions by using MIN-MAX values as fuzzy data. For the face candidate region, the eyes are detected from C channel of color space CMY, and the mouth from Q channel of color space YIQ. The face region is tracked seeking the features of the eyes and the mouth detected from knowledge-base. Experiment includes 1,500 frames of the video images from 10 subjects, 150 frames per subject. The result shows 95.7% of detection rate (the motion areas of 1,435 frames are detected) and 97.6% of good face tracking result (1,401 faces are tracked).