• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.163-169
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• 2017

There are various algorithms evaluating a threshold for image segmentation. Among them, Otsu's algorithm sets a threshold based on the histogram. It finds the between-class variance for all over gray levels and then sets the largest one as Otsu's optimal threshold, so we can see that Otsu's algorithm requires a lot of the computation. In this paper, we improved the amount of computational needs by using estimated Otsu's threshold rather than computing for all the threshold candidates. The proposed algorithm is compared with the original one in computation amount and accuracy. we confirm that the proposed algorithm is about 29 times faster than conventional method on single processor and about 4 times faster than on parallel processing architecture machine.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1327-1342
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• 2010

With the wide deployment of biometric authentication systems, several issues pertaining security and privacy of the biometric template have gained great attention from the research community. To resolve these issues, a number of biometric template protection methods have been proposed. However, the design of a template protection method to satisfy four criteria, namely diversity, revocability and non-invertibility is still a challenging task, especially performance degradation when template protection method is employed. In this paper, we propose a novel method to generate a revocable minutiae-based fingerprint template. The proposed method consists of feature extraction from fingerprint minutiae pairs, quantization, histogram binning, binarization and eventually binary bit-string generation. The contributions of our method are two fold: alignment-free and good performance. Various experiments on FVC2004 DB1 demonstrated the effectiveness of the proposed methods.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.288-292
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• 2016

In this paper, we propose an infrared image based human victim recognition method for a search and rescue robot in dark environments, like general disaster situations. For recognizing a human victim, an infrared camera on a RGB-D camera, Microsoft Kinect, is used. The contrast and brightness of the infrared image are first improved by histogram equalization, and the noise on the image is removed by morphological operation and Gaussian filtering. For recognizing a human victim, the binarization and blob labeling methods are applied to the improved image. Finally, for verifying the effectiveness and feasibility of the proposed method, an experiment for human victim recognition is carried out in a dark environment.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.145-156
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• 2003

This paper for real time object tracking in this treatise detect histogram analysis that is accumulation value of binary conversion density and edge information and body that move by real time use of difference Image techniques and proposed method to object tracking. Firstly, we extract edge that can reduce quantity of data keeping information about form of input image in object detection. Object is extracted by performing difference image and binarization in edge image. Area of detected object is determined by threshold value that divide sum of horizontal accumulation value about binary conversion density by value that add horizontalityㆍverticality maximum accumulation value. Object is tracked by comparing similarity with object that is detected in previous frame and present frame. As experiment result, proposed algorithm could improve the object detection speed, and could track object by real time and could track local movement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1148-1152
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• 2012

In this paper, we propose an intelligent surveillance system using fuzzy contrast and HOG method. This surveillance system is mainly for the intruder detection. In order to enhance the brightness difference, we apply fuzzy contrast and also apply subtraction method to before/after the surveillance. Then the system identifies the intrusion when the difference of histogram between before/after surveillance is sufficiently large. If the incident happens, the camera stops automatically and the analysis of the screen is performed with fuzzy binarization and Blob method. The intruder is detected and tracked in real time by HOG method and linear SVM. The proposed system is implemented and tested in real world environment and showed acceptable performance in both detection rate and tracking success rate.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.44-51
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• 2009

Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.181-188
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• 2012

An effective extraction method for extracting a coated tongue is proposed in this paper, which is used as the diagnostic criteria in the tongue diagnosis. Proposed method uses the fluorescence response characteristics of the coated tongue that is occurred by using the ultraviolet light. Specially, this method can solved the previous problems including the issue in the limits of the diagnosis environment and in the objectivity of the diagnosis results. In our method, original tongue image is acquired by using the ultraviolet light, and binarization is performed by thresholding a valley-points in the histogram that corresponds to the color difference of tongue body and tongue coating. Final view image is presented to the oriental doctor, after applying the canny-edge algorithm to the binary image, and edge image is added to the original image. In order to evaluate the performance of the our proposed method, after building a various tongue image, we compared the true region of coated tongue by the oriental doctor's hand with the extracted region by the our method. As a result, the proposed method showed the average 87.87% extraction ratio. The shape of the extracted coated tongue region showed also significantly higher similarity.

• Journal of Information Technology Application
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• v.3 no.2
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• pp.133-147
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• 2001

This paper proposed a new algorithm for binarization and removal of noises in color images with characters and pictures. Binarization was performed by threshold which had computed with color-relationship relative to the number of pixel in background and character candidates and pre-threshold for dividing of background and character candidates in input images. The pre-threshold has been computed by the histogram of R, G, B In respect of the images, while background and character candidates of input images are divided by the above pre-threshold. As it is possible that threshold can be dynamically decided by the quantity of the noises, and the character images are maintained and the noises are removed to the maximum. And, in this study, we made the noise pattern table as a result of analysis in noise pattern included in the various color images aiming at removal of the noises from the Images. Noises included in the images can figure out Distribution by way of the noise pattern table and pattern matching itself. And then this Distribution classified difficulty of noises included in the images into the three categories. As removal of noises in the images is processed through different procedure according to the its classified difficulties, time required for process was reduced and efficiency of noise removal was improved. As a result of recognition experiments in respect of extracted characters in color images by way of the proposed algorithm, we conformed that the proposed algorithm is useful in a sense that it obtained the recognition rate in general documents without colors and pictures to the same level.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.798-809
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• 2014

Video stabilization is performed to remove unexpected shaky and irregular motion from a video. It is often used as preprocessing for robust feature tracking and matching in video. Typical video stabilization algorithms are developed to compensate motion from surveillance video or outdoor recordings that are captured by a hand-help camera. However, since the vehicle video contains rapid change of motion and local features, typical video stabilization algorithms are hard to be applied as it is. In this paper, we propose a novel approach to compensate shaky and irregular motion in vehicle video using linear regression model and vertical projection histogram matching. Towards this goal, we perform vertical projection histogram matching at each sub region of an input frame, and then we generate linear regression model to extract vertical translation and rotation parameters with estimated regional vertical movement vector. Multiple binarization with sub-region analysis for generating the linear regression model is effective to typical recording environments where occur rapid change of motion and local features. We demonstrated the effectiveness of our approach on blackbox videos and showed that employing the linear regression model achieved robust estimation of motion parameters and generated stabilized video in full automatic manner.