• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.23-30
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• 2016

In this paper, we propose an adaptive histogram projection technique for dynamic range compression and an efficient detail enhancement method which is enhancing strong edge while reducing noise. First, The high dynamic range image is divided into low-pass component and high-pass component by applying 'guided image filtering'. After applying 'guided filter' to high dynamic range image, second, the low-pass component of the image is compressed into 8-bit with the adaptive histogram projection technique which is using global standard deviation value of whole image. Third, the high-pass component of the image adaptively reduces noise and intensifies the strong edges using standard deviation value in local path of the guided filter. Lastly, the monitor display image is summed up with the compressed low-pass component and the edge-intensified high-pass component. At the end of this paper, the experimental result show that the suggested technique can be applied properly to the IR images of various scenes.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.1004-1019
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• 2021

To solve the problems of the low image contrast, fuzzy edge details and edge details missing in noisy image fusion, this study proposes a noisy infrared and visible light image fusion algorithm based on non-subsample contourlet transform (NSCT) and an improved bilateral filter, which uses NSCT to decompose an image into a low-frequency component and high-frequency component. High-frequency noise and edge information are mainly distributed in the high-frequency component, and the improved bilateral filtering method is used to process the high-frequency component of two images, filtering the noise of the images and calculating the image detail of the infrared image's high-frequency component. It can extract the edge details of the infrared image and visible image as much as possible by superimposing the high-frequency component of infrared image and visible image. At the same time, edge information is enhanced and the visual effect is clearer. For the fusion rule of low-frequency coefficient, the local area standard variance coefficient method is adopted. At last, we decompose the high- and low-frequency coefficient to obtain the fusion image according to the inverse transformation of NSCT. The fusion results show that the edge, contour, texture and other details are maintained and enhanced while the noise is filtered, and the fusion image with a clear edge is obtained. The algorithm could better filter noise and obtain clear fused images in noisy infrared and visible light image fusion.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권1호
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• pp.64-75
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• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 춘계학술발표대회
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• pp.215-218
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• 2013

In this paper, we proposed a method for enhancement of color images with sky regions. The input image is converted into HSV space and then sky and non-sky regions are separated. For sky region, saturation enhancement is performed for each pixel based on the enhancement factor calculated from the average saturation of its local neighborhood. On the other hand, for the non-sky region, the enhancement is applied only on the luminance value (V) component of the HSV color image, which is performed in two steps. The luminance enhancement, which is also called as dynamic range compression, is carried out using nonlinear transfer function. Again, each pixel is further enhanced for the adjustment of the image contrast depending upon the center pixel and its neighborhood pixel values. At last, the original H and V component image and enhanced S component image for the sky region, and original H and S component image and enhanced V component image for the non-sky region are converted back to RGB image.

• 대한전자공학회논문지SP
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• 제43권6호
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• pp.65-84
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• 2006

본 논문에서는 개선된 영상 생성 모델을 제시하고, 제시된 모델에 기반한 칼라 영상 향상을 제안한다. 제시된 영상 생성 모델에서는 입력 영상을 전역 조명 성분과 국부 조명 성분, 그리고 반사율 성분의 곱으로 표현한다. 제안된 칼라 영상 향상에서는 RGB 입력 칼라 영상을 HSV 칼라 영상으로 변환한 다음, 백색광 조명 상태라는 가정 하에 개선된 영상 생성 모델에 근거하여 V 성분 영상만을 향상한다. 전역 조명 성분은 입력 V 성분 영상에 유효 영역이 넓은 선형 저대역 필터를 적용하여 추정하고, 국부 조명 성분은 입력 V 성분 영상에서 추정된 전역 조명 성분이 제거된 영상에 유효 영역이 좁은 JND (just noticeable difference) 기반의 비선형 저대역 필터를 적용하여 추정한다. 그리고 반사율 성분은 입력 V 성분 영상에 추정된 전역 조명 성분과 국부 조명 성분을 나누어 추정한다. 이어서 이들 추정된 성분에 감마 수정을 각각 적용하고 그 결과들을 곱하여 출력 V 성분 영상을 얻은 다음 히스토그램 모델링을 적용하여 최종 출력 V 성분 영상을 얻는다. 마지막으로 최종 출력 V 성분 영상과 입력 H 성분 영상 및 S 성분 영상으로부터 출력 RGB 칼라 영상을 얻는다. 실험 결과 제안된 방법은 NASA 홈 페이지로부터 다운받은 칼라 영상과 MPEG-7 CCD 칼라 영상으로 구축한 시험 영상 데이터 베이스에 대하여 후광 효과가 거의 억제되고 색상 변화가 거의 없으면서 전역 대비와 국부 대비를 동시에 잘 증가시키는 것을 확인하였다.

• 한국멀티미디어학회논문지
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• 제12권7호
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• pp.947-952
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• 2009

본 논문에서는 DIP 연산자를 이용한 컬러 스케치 영상 생성 방법을 제안한다. 제안된 방법에서는 먼저 입력 RGB 컬러 영상을 HSV 컬러 영상으로 변환한 다음 밝기 성분인 V 성분 영상에 DIP 연산자를 적용하여 V 성분 스케치 영상을 추출한다. 추출된 V 성분 스케치 영상은 시각적 편의를 위하여 반전과 대비 신장 과정을 거친다. S 성분 영상은 출력 컬러 스케치 영상의 컬러가 입력 영상의 컬러와 같으면서 약간 진하게 나타나도록 향상 과정을 거친다. 이들 S 성분 및 V 성분 영상들은 원래의 H 성분 영상과 함께 RGB 컬러 영상으로 변환되어 출력 컬러 스케치 영상을 얻는다. 실험 결과 제안된 방법은 시험 영상에 대하여 원 영상의 컬러를 잘 살리면서 손으로 그린 스케치화와 유사한 컬러 스케치 영상을 생성함을 보여준다.

• 제어로봇시스템학회논문지
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• 제16권11호
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• pp.1089-1095
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• 2010

We propose an image segmentation method for auto-teaching system of PCB (Printed Circuit Board) assembly inspection machines. The inspection machine acquires images of all components in PCB, and then compares each image with its standard image to find the assembly errors such as misalignment, inverse polarity, and tombstone. The component window that is the area of component to be acquired by camera, is one of the teaching data for operating the inspection machines. To reduce the teaching time of the machine, we newly develop the image processing method to extract the component window automatically from the image of PCB. The proposed method segments the component window by excluding the soldering parts as well as board background. We binarize the input image by use of HSI color model because it is difficult to discriminate the RGB colors between components and backgrounds. The linear combination of the binarized images then enhances the component window from the background. By use of the horizontal and vertical projection of histogram, we finally obtain the component widow. The experimental results are presented to verify the usefulness of the proposed method.

• 전기학회논문지
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• 제61권3호
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• 대한임베디드공학회논문지
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• 제6권1호
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• pp.48-54
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• 2011

As personal or compact devices with image acquisition functionality are becoming easily available for common users, the voluminous images that need to be managed by image related services or systems demand efficient and effective methods in the perspective of image identification. The objective of image identification is to associate an image with a unique identifier. Moreover, whenever an image identifier needs to be regenerated, the newly generated identifier should be consistent. In this paper, we propose three image identifier generation methods utilizing image features: linear component, luminance area, and combination of both features. The linear component based method exploits the information of distribution of partial lines over an image, while the luminance area based method utilizes the partition of an image into a number of small areas according to the same luminance degree. The third method is proposed in order to take advantage of both former methods. In this paper, we also demonstrate the experimental evaluations for uniqueness and similarity analysis that have shown favorable results.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2001년도 Bulletin of The 5th Asian Design Conference International Symposium on Design Science
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• pp.60.2-60
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• 2001