• The Journal of the Society of Korean Medicine Diagnostics
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• v.17 no.2
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• pp.77-89
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• 2013

Objectives Tongue diagnosis is an important diagnostic method in traditional Eastern medicine, and it has a high potential to be used in the future healthcare because of easy, quick, and non-contact measuring features. Recently, research and development efforts on computerized tongue diagnosis systems (CTDS) have been active that led to the technical advancements in the field of photographing techniques, image extraction and classification algorithms. In this study, we analyzed the trends in the CTDS patents. Using the WIPS search engine (www.wipsglobal.com), quantitative and qualitative patent analyses were performed through Korea, China, Japan, U.S.A and Europe. Methods For a systematic search and data analysis, we defined patent categories based on the application area and technical details. By applying thus-obtained categorical key words, we obtained 360 relevant patents on photographing techniques, image extraction and classification algorithms for the purpose of diagnosis or security. Results As a result, companies related to image acquisition, medical imaging and mobile devices and research groups of universities in East Asia were major patent applicants. In all the five countries, the number of patents have been increasing since 1980. In particular, technology related to color correction and image segmentation were most actively patented categories, and expected to continue a high application rate.

• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.585-595
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• 2006

The purpose of this study is to classify the fashion images on sportive style, to find out the difference between the image of sportive style which consumers prefer and the image of sportive style which they want to show and, finally, to analyze their purchase behavior. This research is done with survey method. The subjects of the survey are 835 females in their twenties or their thirties in Pusan area. The data are analyzed with factor analysis, Cronbach's alpha, $X^2$-test, and frequency analysis. The results of this study are as follows: first, sportive style is classified into Sexy, Romantic, Active and Modem image. Second, the results of analysis on consumers' preferring image and their wanting-to-show image to the above-mentioned image classification are as follows: firstly, the subjects' most preferring image and the image which they most want to show is Modem in1age. The second is Sexy image. But the subjects preferred having Modem image. Secondly, consumers' Individuality and apparel's Function are the important reasons to choose the sportive style. Thirdly, Modem image is the most preferred in the images of street wear. Sexy image and Active image are the preferred in the images of sports wear. Third, It is a vivid tone and a dark tone that is the color tone of sportive wear which consumers prefer. They prefer a logo- patterned sports wear, too. The consumers obtain most information on sports wear from sports wear stores. Silhouette is the most decisive design element in consumers' purchasing. The sports wear brands which the subjects prefer are Adidas and Nike.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• International Journal of Contents
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• v.7 no.4
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• pp.24-29
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• 2011

In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.5
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• pp.241-246
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• 2015

Although many different methods have been used to solve stereo correspondent problems, the deviation of accuracy is too big. Among those many methods, the one that uses segmentation information of input image has received high attention in academic field since it is very close to vision recognition. In this thesis, the existing method of acquiring a single value by using the segment information and initial disparity value was viewed in NP-hard problem to propose a new method. In order to verify the validity of the proposed method, well-known data were used for experiment and the resulted data was analyzed. Although there were some disadvantages in the time aspect, it showed somewhat useful results in the accuracy aspect.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.901-914
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• 2002

In this paper, We propose an efficient automatic caption detection and location method, caption recognition using FE-MCBP(Feature Extraction based Multichained BackPropagation) neural network for content based retrieval of video. Frames are selected at fixed time interval from video and key frames are selected by gray scale histogram method. for each key frames, segmentation is performed and caption lines are detected using line scan method. lastly each characters are separated. This research improves speed and efficiency by color segmentation using local maximum analysis method before line scanning. Caption detection is a first stage of multimedia database organization and detected captions are used as input of text recognition system. Recognized captions can be searched by content based retrieval method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1128-1139
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• 2012

With the development of three-dimensional display and related technologies, depth video coding becomes a new topic and attracts great attention from industries and research institutes. Because (1) the depth video is not a sequence of images for final viewing by end users but an aid for rendering, and (2) depth video is simpler than the corresponding color video, fast algorithm for depth video is necessary and possible to reduce the computational burden of the encoder. This paper proposes a fast mode decision algorithm for depth video coding based on depth segmentation. Firstly, based on depth perception, the depth video is segmented into three regions: edge, foreground and background. Then, different mode candidates are searched to decide the encoding macroblock mode. Finally, encoding time, bit rate and video quality of virtual view of the proposed algorithm are tested. Experimental results show that the proposed algorithm save encoding time ranging from 82.49% to 93.21% with negligible quality degradation of rendered virtual view image and bit rate increment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.11
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• pp.2051-2057
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• 2007

In Oriental medicine, the status of a tongue is the important indicator to diagnose one's health like physiological and clinicopathological changes of inner parts of the body. The method of tongue diagnosis is not only convenient but also non-invasive and widely used in Oriental medicine. However, tongue diagnosis is affected by examination circumstances a lot like a light source, patient's posture and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue is inevitable but difficult since the colors of a tongue, lips and skin in a mouth are similar. The proposed method includes preprocessing, graph-based over-segmentation, detecting positions with a local minimum over shading, detecting edge with color difference and estimating edge geometry from the probable structure of a tongue, where preprocessing performs down-sampling to reduce computation time, histogram equalization and edge enhancement. A tongue was segmented from a face image with a tongue from a digital tongue diagnosis system by the proposed method. According to three oriental medical doctors' evaluation, it produced the segmented region to include effective information and exclude a non-tongue region. It can be used to make an objective and standardized diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.65-70
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• 2023

In this paper, we used a deep learning model to detect and segment flame and smoke in real time from fires. To this end, well known U-NET was used to separate and divide the flame and smoke of the fire using multi-class. As a result of learning using the proposed technique, the values of loss error and accuracy are very good at 0.0486 and 0.97996, respectively. The IOU value used in object detection is also very good at 0.849. As a result of predicting fire images that were not used for learning using the learned model, the flame and smoke of fire are well detected and segmented, and smoke color were well distinguished. Proposed method can be used to build fire prediction and detection system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.428-434
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• 2015

A new tongue examination model by using color analysis according to the taste division of tongue. The proposed system consists of a tongue image acquisition in a predefined template, taste region segmentation, color distribution analysis and abnormality decision of tongue by color analysis using Hue-Saturation histograms and the part of a mobile application service. We divided 4 basic taste(bitter, sweet, salty and sour) regions and performed color distribution analysis targeting each region under HSI(Hue Saturation Intensity) color model. To minimize the influence of illumination, the histograms of H and S components only except U are utilized. Using the analyzed results, the abnormality is discriminated by the criteria of the histogram range of normal tongues. Finally, a self tongue diagnosis system which can be used anytime and anywhere on mobile environment.