• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.35-44
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• 2010

In this paper, we propose a method about producing skin color model adaptively in input image and face detection. The principle process which we proposed is finding eyes candidates by applying the eye features to neural network, and then using the around color to find the distribution of color value. There will be a verification process that producing face region by using color value distribution which is detected as skin region and find mouth candidate in corresponding face region; if eye candidate and mouth candidate's connection structure is similar with face structure, then it can be judged as a face. Because this method can detect skin region adaptively by finding eyes, we solve the rate of false positive about the distorted skin color which is used by existing face detection methods. The experiment was performed about detecting the eye, the skin, the mouth and the face individually. The results revealed that the proposed technique is better than the traditional techniques.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.12
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• pp.105-110
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• 2016

To reduce the cost and volume of a digital camera, a subsampled color filter array(CFA) image is used and demosaicking is applied to estimate the missing color values. However, aliasing, the overlaps of signals in the frequency domain, occurs when signals are subsampled. This causes aliasing artifacts such as false colors and zipper effects in demosaicking processes. In this paper, the algorithm estimating high-quality color images by removing aliasing artifacts in them is proposed. The aliasing region map is estimated using the sub-sampled signals of the CFA image. By using the aliasing region map and the estimated luminance image, the least squares problem of the observation models is designed and aliasing artifacts are eliminated. The experiments demonstrate that the proposed algorithm restores color images without aliasing artifacts.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.59-66
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• 2011

In this paper, we propose a face detection method based on color constancy and geometrical analysis. With the problem about the various colors of skin under scene illuminant, a color constancy method is applied to input images and geometrical analysis is used to detect face regions. At first, the candidates of face or hair are extracted from the image that a color constancy method is applied to, and are classified by some geometrical criterions. And then, face candidates which have some intersectional regions whose total is over a certain size, with hair candidates are selected as faces. Caltech Face DB was used to compare the performance of our method. Also, performance about scene illuminant was evaluated by images which have some illumination effects. The experiment results show that the proposed face detection method was applicable to various facial images because of high true-positive and low false-negative ration.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.26-36
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• 2006

In this paper, we propose a cost-effective color filter may (CFA) demosaicing method for digital still cameras in which a single CCD or CMOS image sensor is used. Since a CFA is adopted, we must interpolate missing color values in the red, green and blue channels at each pixel location. While most state-of-the-art algorithms invest a great deal of computational effort in the enhancement of the reconstructed image to overcome the color artifacts, we focus on eliminating the color artifacts with low computational complexity. Using spatial correlation of the adjacent pixels, the edge-directional information of the neighbor pixels is used for determining the edge direction of the current pixel. We apply our method to the state-of-the-art algorithms which use edge-directed methods to interpolate the missing color channels. The experiment results show that the proposed method enhances the demosaiced image qualify from $0.09{\sim}0.47dB$ in PSNR depending on the basis algorithm by removing most of the color artifacts. The proposed method was implemented and verified successfully using verilog HDL and FPGA. It was synthesized to gate-level circuits using 0.25um CMOS standard cell library. The total logic gate count is 12K, and five line memories are used.

• Journal of Physiology & Pathology in Korean Medicine
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• v.33 no.3
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• pp.163-168
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• 2019

In order to enhance the 8 principle pattern diagnosis rate comparing with diagnostic method by self-report questionnaire on cold/heat pattern in the clinical practice, a new diagnostic method using form-color-pulse-symptom (FCPS) system is proposed. FCPS system is composed of outputs of cold/heat pattern through the calculation process of contribution degree to the cold, heat pattern and qi, blood, yin, yang deficiency patterns, based on analysis of 16 mechanisms of disease calculated by diagnostic system of oriental medicine (DSOM) first. And second component is an output of differentiated 8 principle patterns in detail through binding and calculating process with digital informations of pulse, color, form, constitution obtained by computerized measurement system. Putting together above two processes consecutively, cold-heat complex or true/false cold/heat patterns and personalized characters of cold/heat patterns of each patient can be subdivided through a computation method of determining each pattern. In conclusion, 8 principle pattern identification can be performed more accurately using FCPS system than existent self report questionnaire method. These hypothetic proposal is needed to be proven by clinical trial for the future and then the accurate numbers used in each calculational function should be revised properly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.381-404
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• 2022

Forest fires inflict great losses of human lives and serious damages to ecological systems. Hence, numerous fire detection methods have been proposed, one of which is fire detection based on sensors. However, these methods reveal several limitations when applied in large spaces like forests such as high cost, high level of false alarm, limited battery capacity, and other problems. In this research, we propose a novel forest fire detection method based on image processing and correlation coefficient. Firstly, two fire detection conditions are applied in RGB color space to distinguish between fire pixels and the background. Secondly, the image is converted from RGB to YCbCr color space with two fire detection conditions being applied in this color space. Finally, the correlation coefficient is used to distinguish between fires and objects with fire-like colors. Our proposed algorithm is tested and evaluated on eleven fire and non-fire videos collected from the internet and achieves up to 95.87% and 97.89% of F-score and accuracy respectively in performance evaluation.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.174-184
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• 2008

Complementary color filter array (CCFA) is widely used in consumer-level digital video cameras, since it not only has high sensitivity and good signal-to-noise ratio in low-light condition but also is compatible with the interlaced scanning used in broadcast systems. However, the full-color images obtained from CCFA suffer from the color artifacts such as false color and zipper effects. These artifacts can be removed with edge-adaptive demosaicking (ECD) approaches which are generally used in rrimary color filter array (PCFA). Unfortunately, the unique array pattern of CCFA makes it difficult that CCFA adopts ECD approaches. Therefore, to apply ECD approaches suitable for CCFA to demosaicking is one of the major issues to reconstruct the full-color images. In this paper, we propose a new ECD algorithm for CCFA. To estimate an edge direction precisely and enhance the quality of the reconstructed image, a function of spatial variances is used as a weight, and new color conversion matrices are presented for considering various edge directions. Experimental results indicate that the proposed algorithm outperforms the conventional method with respect to both objective and subjective criteria.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.