• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.173-181
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• 2004

In this paper, we propose a system for automatic moving object detection and tracking in sequence images acquired from a moving camera. The proposed algorithm consists of moving object detection and its tracking. Moving object can be detected by integration of BBME and DD method We segment the detected object using histogram back projection, match it using histogram intersection, extract and track it using XY-projection. Computer simulation results have shown that the proposed algorithm is reliable and can successfully detect and track a moving object on image sequences obtained by a moving camera.

• Journal of KIISE
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• v.44 no.3
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• pp.295-305
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• 2017

Facial appearance is greatly influenced by illumination conditions, and therefore illumination variation is one of the factors that degrades performance of face recognition systems. In this paper, we propose a robust method for face representation under varying illumination conditions, combining non-alpha Weberface (non-alpha WF) and histogram equalization. We propose a two-step method: (1) for a given face image, non-alpha WF, which is not applied a parameter for adjusting the intensity difference between neighboring pixels in WF, is computed; (2) histogram equalization is performed to non-alpha WF, to make a uniform histogram distribution globally and to enhance the contrast. $(2D)^2PCA$ is applied to extract low-dimensional discriminating features from the preprocessed face image. Experimental results on the extended Yale B face database and the CMU PIE face database show that the proposed method yielded better recognition rates than several illumination processing methods as well as the conventional WF, achieving average recognition rates of 93.31% and 97.25%, respectively.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2294-2301
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• 1998

The aim of this paper is to extract features from each news scenes for example, symbol icon which can be distinct each broadcasting corp, icon and caption which are has feature and important information for the scene in respectively, In this paper, we propose extraction methods of caption that has important prohlem of news videos and it can be classified in three steps, First of al!, we converted that input images from video frame to YIQ color vector in first stage. And then, we divide input image into regions in clear hy using equalized color histogram of input image, In last, we extracts caption using edge histogram based on vertical and horizontal line, We also propose the method which can extract news icon in selected key frames by the difference of inter-histogram and can divide each scene by the extracted icon. In this paper, we used comparison method of edge histogram instead of complex methcxls based on color histogram or wavelet or moving objects, so we shorten computation through using simpler algorithm. and we shown good result of feature's extraction.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.162-170
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• 2013

In recent years, the interests and needs of the Pedestrian Protection System (PPS), which is mounted on the vehicle for the purpose of traffic safety improvement is increasing. In this paper, we propose a pedestrian candidate window extraction and unit cell histogram based HOG descriptor calculation methods. At pedestrian detection candidate windows extraction stage, the bright ratio of pedestrian and its circumference region, vertical edge projection, edge factor, and PCA reconstruction image are used. Dalal's HOG requires pixel based histogram calculation by Gaussian weights and trilinear interpolation on overlapping blocks, But our method performs Gaussian down-weight and computes histogram on a per-cell basis, and then the histogram is combined with the adjacent cell, so our method can be calculated faster than Dalal's method. Our PCA reconstruction error based pedestrian detection candidate window extraction method efficiently classifies background based on the difference between pedestrian's head and shoulder area. The proposed method improves detection speed compared to the conventional HOG just using image without any prior information from camera calibration or depth map obtained from stereo cameras.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4472-4477
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• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.2
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• pp.21-28
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• 2008

This paper proposes a novel reduced-reference assessment method for video quality assessment, in which one-dimensional (1-D) histograms of motion vectors (MVs) are used as features of videos. The proposed method is more efficient than the conventional methods in view of computation time, because the proposed quality metric decodes MVs directly from video stream in the parsing process instead of reconstructing the distorted video at the receiver. Moreover, in view of data size, the propose method is efficient because a sender transmits 1-D histograms of MVs accumulated over whole input video sequences. Here, we use 1-D histograms of MVs accumulated over the whole video sequences, which is different from the conventional methods that assessed each image independently. For testing the similarity between histograms, we use histogram intersection and histogram difference methods. We compare the proposed method with the conventional methods for 52 video clips, which are coded under varying bit rate, image size, and frame rate. Experimental results show that the proposed method is more efficient than the conventional methods and that the proposed method is more similar to the mean opinion score (MOS) than conventional algorithms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.441-447
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• 2023

Intravenous infusion therapy is a standard nursing procedure in medical institutions that provides patients with drugs, fluids, blood, and nutrients into the patient's mucus. It is mainly performed and managed by nurses. Additionally, it is an injection method that injects drugs directly into the blood vessels, and is used to achieve rapid results in emergency situations, and quick and accurate effects can be expected. Even experienced nurses through education and training often make mistakes, which can not only cause discomfort to patients but also cause various problems that threaten patient safety. Various studies are being conducted to reduce the pain caused by these mistakes. This paper acquired images of veins on the back of the hands of three subjects through an image detection device and conducted a study to derive an algorithm to provide clear vein images through image processing of the collected images. To sharpen the acquired vein images, existing algorithms Histogram Equalization, CLAHE, and Unsharp Masking were selected and combined. A histogram graph was used to compare images derived by applying individual algorithms and algorithm combinations to images. The histogram graph was checked by calculating the difference between the minimum and maximum values of distributed pixels and averaging them. The algorithm combination presented in this paper was 209.1, which was higher than the average values of individual algorithms of 138.7, 132.3, and 126.2, and it was confirmed that visibility was good even in actual images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.93-101
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• 2007

To jointly optimize the spatial registration and the exposure compensation, an iterative registration algorithm, the Lucas-Kanade algorithm, is combined with an exposure compensation algorithm, which is based on the histogram transformation function. Based on a simple regression model, a nonparametric estimator, the empirical conditional mean, and its polynomial fitting are used as histogram transformation functions for the exposure compensation. Since the proposed algorithm is composed of separable optimization phases, the proposed algorithm is more advantageous than the joint approaches of Mann and Candocia in the aspect of implementation flexibility. The proposed algorithm performs a better registration for real images than the case of registration that does not consider the exposure difference.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.163-165
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• 2011

In this paper we collect data concerning sleep environments in a bedroom and analyze the relationship between the collected condition data and sleep. In addition, this paper detects scene changes from the subjects in a sleeping state and presents the physical conditions, reactions during sleep, and physical sensations and stimuli. To detect scene changes in image sequences, we used color histogram for the difference between the preceding frame and the current frame. In addition, to extract the tossing and turning for different situations, the subjects were instructed to enter the level of fatigue, the level of drinking, and the level of stomach emptiness.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.76-82
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• 2010

Reversible watermarking hides some information in a digital image in such a way that an authorized party could decode the hidden information and also restore the image to its original state. In this paper, a high capacity reversible watermarking method using histogram shifting is proposed. In order to increase embedding capacity, the proposed method divides the image into $2{\times}2$ blocks and uses a paring(horizontal, vertical, diagonal) inside each block, then finds a maximum embedding bin which has the most frequent difference values among the parings. Also, the proposed method removes the overflow and underflow by using location map which including the maximum embedding bin and increases the embedding capacity by embedding iteratively. The experimental results show that the proposed method provides a high embedding capacity and good visual quality compared with the conventional reversible watermarking methods.