Entropy 기반의 Weighted FCM 알고리즘을 이용한 컬러 영상 Multi-level thresholding

오준택,곽현욱,김욱현,Oh, Jun-Taek,Kwak, Hyun-Wook,Kim, Wook-Hyun 대한전자공학회 2005 電子工學會論文誌-SP (Signal processing) Vol.42 No.6

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods. 본 논문은 weighted FCM(Fuzzy C-Means) 알고리즘을 적용한 컬러 영상 multi-level thresholding을 제안한다. FCM 알고리즘은 기존의 thresholding 방법들과 달리 최적의 임계치를 결정할 수 있으며 multi-level thresholding으로의 확장이 가능하다. 그러나 공간정보를 포함하고 있지 않기 때문에 잡음 등에 민감하다는 단점을 가진다. 본 논문은 이러한 단점을 해결하기 위해서 이웃 화소들로부터 얻은 entropy 기반의 가중치(weight)를 FCM 알고리즘에 적용함으로써 잡음의 제거가 가능하다. 그리고 각 색상별 성분의 군집 화소들을 기반으로 생성한 코드 영상에 대해서 군집 내부의 거리값을 이용하여 최적의 군집수를 결정한다. 실험에서 제안한 방법이 기존의 방법들보다 잡음에 대해서 강건하며 우수한 분할 성능을 보였다.

An image thresholding method based on the target extraction

Yunjie Zhang,Yi Li,Zhijun Gao,Weina Wang 한국전산응용수학회 2008 Journal of applied mathematics & informatics Vol.26 No.3-4

In this paper an algorithm, based on extracting a certain target of an image, is proposed that is capable of performing bilevel threshold- ing of image with multimodal distribution. Each pixel in the image has a membership value which is used to denote the characteristic relationship between the pixel and its belonging region (i.e. the object or background). Using the membership values of image set, a new measurement, which simultaneously measures the measure of fuzziness and the conditional en- tropy of the image, is calculated. Then, thresholds are found by optimally minimizing calculated measurement. In addition, a fuzzy range is defined to improve the threshold values. The experimental results demonstrate that the proposed approach can select the thresholds automatically and ef- fectively extract the meaningful target from the input image. The resulting image can preserve the object region we target very well. In this paper an algorithm, based on extracting a certain target of an image, is proposed that is capable of performing bilevel threshold- ing of image with multimodal distribution. Each pixel in the image has a membership value which is used to denote the characteristic relationship between the pixel and its belonging region (i.e. the object or background). Using the membership values of image set, a new measurement, which simultaneously measures the measure of fuzziness and the conditional en- tropy of the image, is calculated. Then, thresholds are found by optimally minimizing calculated measurement. In addition, a fuzzy range is defined to improve the threshold values. The experimental results demonstrate that the proposed approach can select the thresholds automatically and ef- fectively extract the meaningful target from the input image. The resulting image can preserve the object region we target very well.

Saliency Map을 이용한 최적 임계값 기반의 객체 추출

Nguyen Cao Truong Hai,김도연(Do-Yeon Kim),박혁로(Hyuk-Ro Park) 한국콘텐츠학회 2011 한국콘텐츠학회논문지 Vol.11 No.6

이미지로부터 중요 객체를 추출하는 것은 추적, 분할, 적응적 압축, 내용기반 검색과 같은 멀티미디어 처리에 있어서 매우 중요한 부분이며, 현재 이에 관한 많은 연구가 진행 되고 있다. 중요 객체 추출을 위한 방법으로 Saliency Map을 이용한 방법이 있다. 이 방법에서는 일반적으로 이진화된 Saliency Map을 이용하여 어떤 화소가 중요 객체 내부인가 아닌가를 표시한다. 따라서 이 방법은 이진화를 위한 임계값의 선택이 성능에 매우 중요한 영향을 끼친다. 기존 연구에서는 일반적으로 휴리스틱 방법을 이용하여 임계값을 결정하거나 매개변수로 임계값을 조정하는 방법이 사용되었다. 그러나 하나의 임계값 적용은 이미지 안의 다수의 객체가 포함되어 있는 경우 적합하지 않다. 본 논문에서는 이러한 단점을 개선할 수 있는 Otsu 임계값을 이용한 전역적인 최적 임계값을 사용하는 방법을 제안한다. 제안하는 Otsu 임계화 방법은 단일-계층에 적용할 수 있는 Otsu 방법과 이를 확장하여 다중-계층에도 적용할 수 있는 Otsu 방법이다. 제안한 방법을 기존의 Saliency Map 모델에 적용한 결과 성능이 개선되었음을 확인하였다. Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.

Automatic Segmentation of Skin and Bone in CT Images using Iterative Thresholding and Morphological Image Processing

Kang, Ho Chul,Shin, Yeong-Gil,Lee, Jeongjin The Institute of Electronics and Information Engin 2014 IEIE Transactions on Smart Processing & Computing Vol.3 No.4

This paper proposes a fast and efficient method to extract the skin and bone automatically in CT images. First, the images were smoothed by applying an anisotropic diffusion filter to remove noise. The whole body was then detected by thresholding, which was set automatically. In addition, the contour of the skin was segmented using morphological operators and connected component labeling (CCL). Finally, the bone was extracted by iterative thresholding.

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto,Esti Suryani,Murdoko Susilo 한국지능시스템학회 2019 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.19 No.4

Blood vessels in the retina of the eye are one important sign when making a diagnosis of hypertensive retinopathy. On the retina can be known several signs including tortuosity and arteriovenous ratio. Blood vessels mixed with a number of objects in the retina, the segmentation of blood vessels becomes a very interesting challenge because they have to separate blood vessels from a number of objects. This study aims to segmentation blood vessels using the main method of self-organizing maps artificial neural networks (SOMANN). The proposed segmentation method is divided into three stages, namely preprocessing, segmentation, and performance analysis. The preprocessing step is to improve image quality using the contrast-limited adaptive histogram equalization (CLAHE), median filter, and morphology. The segmentation stage uses the SOM-ANN algorithm combined with the mean or median thresholding. The performance parameters which are measured consist of sensitivity, specificity, and area under the curve (AUC). The test results using the dataset STARE and DRIVE show that the median thresholding is able to provide the best AUC performance compared to the mean thresholding. The proposed segmentation model is able to provide performance in the excellent category, with AUC values of 90.55% for the STARE dataset and 90.35% for the DRIVE.

시공간 엔트로피 임계법과 카메라 패닝 보상을 이용한 객체 기반 동영상 분할

백경환,곽노윤 한국산학기술학회 2003 한국산학기술학회논문지 Vol.4 No.3

본 논문은 비디오 시퀸스에 카메라 패닝 보상과 2차원 시공간 엔트로피 임계법을 적용하여 추출한 객체포함영역을 대상으로 영상 분할을 수행하는 이동객체 분할 기법에 관한 것이다. 우선, 웨이블렛 변환에 의해 구성한 피라미드 계층 구조상에서 카메라 패닝 벡터를 추정하여 전역 움직임을 보상한다. 이후, 전역 움직임이 보상된 기준영상을 대상으로 각 프레임간에서 2차원 시공간 엔트로피 임계법을 적용하여 이동 객체가 포함될 가능성이 있는 영역을 블록 단위로 추출한다 다음으로, 2차원 시공간 엔트로피 임계법에 의해 분류된 영역을 토대로 각 블록을 움직임 블록, 준 움직임 블록, 비 움직임 블록 중 어느 하나로 분류한 검색 테이블을 작성한다. 이어서, 검색 테이블을 참조하여 초기 탐색 계층 및 탐색 영역을 적응적으로 선정함으로써 피라미드 계층 구조상에서 효율적인 고속 움직임 추정을 수행하여 이동 객체에 해당하는 객체포함영역만을 추출한다. 최종적으로, 이렇게 추출된 객체포함영역에서 임계 기울기 영상을 정의한 후, 이를 기준 삼아 객체포함영역에 화소단위의 형태학 기반 영상 분할 알고리즘을 적용함으로써 비디오 시퀸스에 포함된 이동 객체를 분할한다. 컴퓨터 시뮬레이션 결과를 통해 고찰할 때, 제안된 방법은 이동 객체에 대한 상대적으로 우수한 분할 특성을 제공할 수 있고, 특히 저대조 경계면의 분할 특성을 제고시키고 있음을 확인할 수 있다. This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

흉부 CT 영상의 밝기값 정보를 사용한 폐 구조물 자동 분할

임예니(Yeny Yim),홍헬렌(Helen Hong) 한국정보과학회 2006 정보과학회논문지 : 소프트웨어 및 응용 Vol.33 No.11

본 논문에서는 흉부 CT 영상의 밝기값 정보를 사용하여 폐 구조물을 자동 분할하기 위한 방법을 제안한다. 본 제안방법은 다음과 같은 다섯 단계로 구성된다. 첫 번째, 영상의 밝기값 차이를 이용하여 폐 구조물을 분할하기 위해 최적 임계값 기법을 사용하여 임계값을 계산한다. 두 번째, 흉부 CT 영상에 2차원 영역성장법의 역 연산을 사용하여 배경으로부터 흉부를, 흉부로부터 기관지 및 폐를 단계적으로 분할한다. 이 때, 밝기값이 비슷한 다른 영역들을 3차원 연결화소군 레이블링을 통해 제거한다. 세 번째, 흉부 CT 영상에 3차원 분기 기반 영역성장법을 적용하여 기관과 좌우 기관지를 분할한다. 네 번째, 기관지 및 폐에서 기관지를 영상 감산함으로써 정확한 폐 영역을 얻는다. 마지막으로, 히스토그램 분석을 통해 임계값을 계산하고 기관지 및 폐에 밝기값 기반 임계값 기법을 적용하여 폐혈관을 분할한다. 제안방법의 정확성을 검증하기 위해 폐, 기관지, 폐혈관의 분할 결과에 대해 육안평가를 수행한다. 제안한 3차원 분기기반 영역성장법을 통한 기관지 분할 결과를 평가하기 위해 기존 영역성장법으로 분할한 결과와 비교한다. 실험 결과는 제안 분할 방법이 폐, 기관지, 폐혈관을 자동으로 정확하게 추출함을 보여준다. We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.

위성영상의 방사적 특성을 고려한 구름 탐지 방법 개발

서원우,강홍기,윤완상,임평채,이수암,김태정 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.6

Clouds cause many difficult problems in observing land surface phenomena using opticalsatellites, such as national land observation, disaster response, and change detection. In addition, thepresence of clouds affects not only the image processing stage but also the final data quality, so it isnecessary to identify and remove them. Therefore, in this study, we developed a new cloud detectiontechnique that automatically performs a series of processes to search and extract the pixels closest to thespectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud maskbased on the threshold. The cloud detection technique largely consists of three steps. In the first step, theprocess of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units wasperformed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation,triangle thresholding, and maximum likelihood classification was applied using the top of the atmospherereflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed andthe cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1L2G images acquired in the Korean Peninsula from April to November, which show the diversity ofspatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method,the results generated by a simple thresholding method were compared. As a result of the experiment,compared to the existing method, the proposed method was able to detect clouds more accurately byconsidering the radiometric characteristics of each image through the preprocessing process. In addition,the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results (F1-score)compared to the existing method but showed limitations in certain images containing snow.

Wavelets and Wavelet Estimation: A Review

Lee, GeungHee THE KOREAN ECONOMIC SOCIETY 1998 JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS Vol.4 No.1

Wavelets have received a lot of attention in statistics since Donoho and Johnstone (1994) introduced wavelet shrinkage estimators, which include some important ideas-wavelets as a new local basis, multiresolution analysis and thresholding. From these ideas, we can reilluminate the methods and data analyses based on Fourier series and truncated estimators. In this paper, we review wavelets, wavelet series estimators, their model selection methods and their applications in econometrics and economics.

택배 자동 분류를 위한 주소영역 검출 알고리즘

김봉석 ( Kim Bong Seog ),김승진 ( Kim Seung Jin ),정윤수 ( Jeong Yun Su ),임성운 ( Im Seong Un ),노철균 ( No Cheol Gyun ),원철호 ( Won Cheol Ho ),조진호 ( Jo Jin Ho ),이건일 ( Lee Geon Il ) 한국센서학회 2003 센서학회지 Vol.12 No.3

본 연구에서는 택배물의 분류를 위한 자동화 시스템에서 주소 영역 검출 알고리즘을 제안하였다. 주소 영역 검출을 위한 알고리즘에서는 대상 영상이 매우 크기 때문에 수행 시간의 단축을 위하여 택배 라벨부분을 포함하는 제한된 범위인 관심영역(Region of interesting: ROI)을 구한 후 관심 영역내에서 모든 알고리즘이 수행되도록 한다. 주소 영역 검출을 위하여 택배 라벨의 특징인 주소 영역을 둘러싸고 있는 테두리선을 이용한다. 이진화(thresholding) 과정과 라벨링 (Labeling) 과정을 통하여 획득된 영상에서 주소 영역의 테두리선과 그 밖의 성분들을 각각 독립된 연결성분들(connected components)로 검출한다. 주소 영역을 둘러싸는 테두리선의 기하학적인 특징을 이용하여 여러 개의 연결성분들 중에서 주소 영역을 둘러싸는 테두리선을 분리한다. 마지막으로 윈 영상과 분리된 테두리선 부분과의 논리적 곱을 이용하여 주소 영역을 최종적으로 검출하게 된다. In this paper, we proposed the algorithm for locating destination address block (DAB) from automatic system to classify packages. For locating DAB, because the size of obtained images is are very large, we select the region of interesting (ROI) to reduce time carrying into algorithm. After selecting the ROI, proposed algorithm is carried out within the ROI. We extract the outline of the handwriting part of the DAB and the rest components within the obtained ROI using thresholding. We carry out labeling to extract each connected component for extracted outline and the rest components. We extract the outline of the handwriting part of the DAB using the geometrical characteristic of the outline of the handwriting part of the DAB among many connected components. The last, we extract the locating DAB using the outline of the handwriting part of the DAB.