• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.826-832
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• 2016

In this paper, we propose a tongue diagnosis system for determining the presence of specific taste crack area as a first step in the digital tongue diagnosis system that anyone can use easily without special equipment and expensive digital tongue diagnosis equipment. Training DB was developed by the Haar-like feature, Adaboost learning on the basis of 261 pictures which was collected in Oriental medicine. Tongue candidate regions were detected from the input image by the learning results and calculated the average value of the HUE component to separate only the tongue area in the detected candidate regions. A tongue area is separated through the Connected Component Labeling from the contour of tongue detected. The palate regions were divided by the relative width and height of the tongue regions separated. Image on the taste area is converted to gray image and binarized with each of the average brightness values. A crack in the presence or absence was determined via Connected Component Labeling with binary images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.930-938
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• 2005

We propose a content-based image retrieval(CBIR) method based on an efncient combination of a color feature and multiresolution texture features. As a color feature, a HSV autocorrelograrn is chosen which is blown to measure spatial correlation of colors well. As texture features, BDIP and BVLC moments are chosen which is hewn to measure local intensity variations well and measure local texture smoothness well, respectively. The texture features are obtained in a wavelet pyramid of the luminance component of a color image. The extracted features are combined for efficient similarity computation by the normalization depending on their dimensions and standard deviation vectors. Experimental results show that the proposed method yielded average $8\%\;and\;11\%$ better performance in precision vs. recall than the method using BDIPBVLC moments and the method using color autocorrelograrn, respectively and yielded at least $10\%$ better performance than the methods using wavelet moments, CSD, color histogram. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.63-70
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• 2012

In medical image processing field, object recognition is usually processed based on region segmentation algorithm. Region segmentation in the computing field is carried out by computerized processing of various input information such as brightness, shape, and pattern analysis. If the information mentioned does not make sense, however, many limitations could occur with region segmentation during computer processing. Therefore, this paper suggests effective region segmentation method based on R2-map information within the magnetic resonance (MR) theory. In this study, the experiment had been conducted using images including the liver region and by setting up feature points of R2-map as seed points for 2D region growing and final boundary correction to enable region segmentation even when the border line was not clear. As a result, an average area difference of 7.5%, which was higher than the accuracy of conventional exist region segmentation algorithm, was obtained.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.633-638
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• 2005

The extraction of a face is a very important part for human interface, biometrics and security. In this paper, we applies DCM(Dilation of Color and Motion) filter and Active Contour Models to extract facial outline. First, DCM filter is made by applying morphology dilation to the combination of facial color image and differential image applied by dilation previously. This filter is used to remove complex background and to detect facial outline. Because Active Contour Models receive a large effect according to initial curves, we calculate rotational degree using geometric ratio of face, eyes and mouth. We use edgeness and intensity as an image energy, in order to extract outline in the area of weak edge. We acquire various head-pose images with both eyes from five persons in inner space with complex background. As an experimental result with total 125 images gathered by 25 per person, it shows that average extraction rate of facial outline is 98.1% and average processing time is 0.2sec.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1487-1494
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• 2012

This paper proposes an algorithm detecting the skew of the degraded as well as the clear document images using edge and correcting it. The proposed algorithm detects edges in a character region selected by image complexity and generates projection histograms by projecting them to various directions. And then it detects the document skew by estimating the edge concentrations in the histograms and corrects the skewed document image. For the fast skew detection, the proposed algorithm uses downsampling and 3 step coarse-to-fine searching. In the skew detection of the clear and the degraded images, the maximum and the average detection errors in the proposed algorithm are about 50% of one in a conventional similar algorithm and the processing time is reduced to about 25%. In the non-uniform luminance images acquired by a mobile device, the conventional algorithm can't detect skews since it can't get valid binary images, while the proposed algorithm detect them with the average detection error of 0.1o or under.

• Journal of the Korean Society of Radiology
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• v.10 no.4
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• pp.263-270
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• 2016

This paper suggests techniques to enhance coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, There applied to 24-bpp color image compression and image techniques. The result did not occur a loss in the image quality of the image when using the encoding method, such as almost to the color in the YIQ image compression rate and image quality, such as RGB images and showed good.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.51-60
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• 2024

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

• Annual Conference of KIPS
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• 2017.04a
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• pp.577-580
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• 2017

중합 효소 연쇄 반응 (PCR) 젤 전기영동 이미지에서 DNA 지문을 분석하기 위한 새로운 레인 검출 및 추적 알고리즘이 제안하였다. 이전에 여러 연구 결과가 보고되었지만 갑작스런 배경 밝기 차이와 구부러진 레인이 있는 이미지에서 레인을 정확하게 추출하는 것은 여전히 어려움이 있다. 우리는 평균 레인 폭과 레인 주기를 계산하기 위한 에지 기반 알고리즘을 제안한다. 본 논문에서 제안한 방법은 k-means 클러스터링 알고리즘을 이용하여 상승 에지와 하강 에지를 정확하게 추출하는 부화소(sub-pixel) 알고리즘을 적용하여 레인 폭과 주기를 추정한다. 구부러진 레인을 처리하기 위해 젤 이미지를 정상영역과 비정상영역으로 분할하고, 각 분할 된 이미지의 레인 중심을 추적한다. 우리가 제안한 방법의 성능을 평가하기 위해 534 레인을 포함한 32 개의 젤 이미지가 사용되었다. 실험 결과는 우리의 방법이 전처리 과정 없이 배경 차이와 구부러진 레인을 갖는 이미지에 강인함을 보여 주었다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.9-12
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• 2017

본 논문에서는 어깨 초음파 영상을 분석하여 인대 손상(Tear) 영역을 추출하는 방법을 제안한다. 제안된 방법은 초음파 영상에서 ROI(Region of Interest) 영역을 추출하고 추출된 ROI 영역에서 사다리꼴 형태의 소속 함수를 적용한 퍼지 스트레칭 기법을 이용하여 명암 대비를 높인다. 명암 대비가 조정된 ROI 영역에서 밝기 평균 이진화 기법을 적용하여 ROI 영역을 이진화한다. 이진화가 적용된 ROI 영역에서 워터쉐드 기법을 적용하여 연골과 힘줄의 후보 영역들을 추출한다. 추출된 연골과 힘줄의 후보 영역들 중에서 위에서 아래로 스캔하여 수평 너비가 가장 큰 영역에 해당하는 힘줄 영역의 상단 경계선을 추출한다. 그리고 아래에서 위로 스캔하여 수평 너비가 가장 큰 영역의 상단 경계에 스플라인 곡선을 적용하여 연골 영역의 상단 경계선을 추출한다. 힘줄 영역의 상단 경계선과 연골 영역의 상단 경계선 양 끝에 2차 함수 곡선을 적용하여 곡선 사이의 양자화할 영역을 추출한 후, SOM 기법을 적용하여 인대 손상 후보 영역을 양자화한다. 양자화된 인대 손상 후보 영역을 분석하여 어깨 힘줄의 손상 영역과 비손상 영역을 구분하고 인대 손상(Tear) 영역을 추출한다. 제안된 방법을 어깨 힘줄이 있는 초음파 영상을 대상으로 실험한 결과, 인대 손상(Tear) 영역이 비교적 정확히 추출되었다.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.209-216
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• 2012

In this paper, we propose a novel tracking method which uses image features consisted of the area, average intensity, aspect ratio of a target image for the real-time IR surveillance system. The image features of the ground target can be modeled as a random process with exponential autocorrelation function mathematically. Finally, we derived a discrete target dynamic equation including kinematic states and geometric states of the target. Simulation results shows that the performance of the proposed method is better than that of the previous tracking method.