• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.715-719
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• 1998

In this paper, we propose a color enhancement system which is based on the improvement of transition time problem and specific color detection stability. The proposcd system apply the time difference correction step to corrects time difference which is taken place at the transimission process between color signal and color subcarrier signal and to reduce detection errors. And also, we proposed stability method to improve transition time problem and detection efficiency as the control of reference color. The proposed system controls specific color when the mean difference value of detected voltages greater than the value of minimum discriminate voltages of two adjacent color signals. Thus, the color enhancement system improves detection efficiency and controls specific color from the color signal without overlapping of correction range.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.589-601
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• 2014

The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.160-167
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• 2014

This paper presents a spliced color image detection method using Benford' Law and color difference. For a suspicious image, after color conversion, the discrete wavelet transform and the discrete cosine transform are performed. We extract the difference between the ideal Benford distribution and the empirical Benford distribution of the suspicious image as features. The difference between Benford distributions for each color component were also used as features. Our method shows superior splicing detection performance using only 13 features. After training the extracted feature vector using SVM classifier, we determine whether the presence of the image splicing forgery. Experimental results show that the proposed method outperforms the existing methods with smaller number of features in terms of splicing detection accuracy.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.116-125
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• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

• 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 Information Processing Systems
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• v.9 no.1
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• pp.89-102
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• 2013

The paper presents a fuzzy based impulse noise filter for both gray scale and color images. The proposed approach is based on the technique of boundary discriminative noise detection. The algorithm is a multi-step process comprising detection, filtering and color correction stages. The detection procedure classifies the pixels as corrupted and uncorrupted by computing decision boundaries, which are fuzzified to improve the outputs obtained. In the case of color images, a correction term is added by examining the interactions between the color components for further improvement. Quantitative and qualitative analysis, performed on standard gray scale and color image, shows improved performance of the proposed technique over existing state-of-the-art algorithms in terms of Peak Signal to Noise Ratio (PSNR) and color difference metrics. The analysis proves the applicability of the proposed algorithm to random valued impulse noise.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.11
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• pp.2330-2334
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• 2009

This paper presented a new scene change detection method of compounding color histogram and $x^2$ histogram. This method overcomes the disadvantages of difference value detection methods and will be taking advantage. Also, this method can detect all from the abrupt scene change detection to gradual scene change detection. The proposed method has been compared with previous method, and our experimental results show the better results than the previous method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.277-280
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• 2007

A moving object tracking algorithm for image searching based on specific color detection is proposed in this paper. That is preprocessed for a luminance variation and noise cancellation to be robust system. The motion tracking is used the difference between input image and reference image in R, G, B each channels for a moving image. The proposed method is enhanced to 15% fast in comparison with the contour tracking method and the matching method, and stable.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.317-326
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• 2004

This paper presents a new method for the detection of multiple pedestrians and tracking of a specific pedestrian using color information from a moving camera. We first extract motion vector on the input image using BMA. Next, a difference image is calculated on the basis of the motion vector. The difference image is converted to a binary image. The binary image has an unnecessary noise. So, it is removed by means of the proposed noise deletion method. Then, we detect pedestrians through the projection algorithm. But, if pedestrians are very adjacent to each other, we separate them using RGB color information. And we track a specific pedestrian using RGB color information in center region of it. The experimental results on our test sequences demonstrated the high efficiency of our approach as it had shown detection success ratio of 97％ and detection failure ratio of 3％ and excellent tracking.

• Journal of Korea Multimedia Society
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• v.10 no.9
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• pp.1135-1142
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• 2007

Front-projection type displays make it difficult to apply traditional skin color detection for human hand because the projected beam not only reaches to the screen but also to the user's hand. This paper solves this problem by modeling the distortion between original image and its final camera input. Our approach improves hand detection rate by modeling of interference effect among color channels and of intra-frame intensity and also by introducing adaptive threshold for color difference in skin region.