• Title/Summary/Keyword: Region-based segmentation

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Image segmentation using adaptive MIN-MAX genetic clustering and fuzzy worm searching (자율 적응 최소-최대 유전 군집호와 퍼지 벌레 검색을 이용한 영상 영역화)

  • 하성욱;서석배;강대성
    • Proceedings of the IEEK Conference
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    • 1998.06a
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    • pp.781-784
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    • 1998
  • An image segmentation approach based on the fuzzy worm searching and MIN-MAx clusterng algorithm is proposed in this paper. This algorithm deals with fuzzy worm value and min-max node at a gross scene level, which investigates the edge information including fuzzy worm action. But current segmentation methods based edge extraction methods generally need the mask information for the algebraic model, and take long run times at mask operation, wheras the proposed algorithm has single operation ccording to active searching of fuzzy worms. In addition, we also genetic min-max clustering using genetic algorithm to complete clustering and fuzyz searching on grey-histogram of image for the optimum solution, which can automatically determine the size of rnages and has both strong robust and speedy calculation. The simulation results showed that the proposed algorithm adaptively divided the quantized images in histogram region and performed single searching methods, significantly alleviating the increase of the computational load and the memory requirements.

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Spatio-Temporal Image Segmentation Using Hierarchical Structure Based on Binary Split Algorithm (이진분열 알고리즘에 기반한 계층적 구조의 시공간 영상 분할)

  • 박영식;송근원;정의윤;한규필;하영호
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 1997.11a
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    • pp.145-149
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    • 1997
  • In this paper, a hierarchical spatio-temporal image segmentation method based on binary split algorithm is proposed. Intensity and displacement vector at each pixel are used for image segmentation. The displacement vectors between two image frames which skip over one or several frames can be approximated by accumulating of the velocity vectors calculated from optical flow between two successive frames when the time interval between the two image frames is short enough or the motion is slow. The pixels whose displacement vector and intensity are ambiguous are precisely decided by the modified watershed algorithm using the proposed priority measure. In the experiment, the region of moving object is precisely segmented.

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Multiple Face Segmentation and Tracking Based on Robust Hausdorff Distance Matching

  • Park, Chang-Woo;Kim, Young-Ouk;Sung, Ha-Gyeong;Park, Mignon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.1
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    • pp.87-92
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    • 2003
  • This paper describes a system for tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

Local Feature Detection on the Ocular Fundus Fluorescein angiogram Using Relaxation Process (이완법을 이용한 형광안저화상의 국소특징 검출)

  • 高昌林
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.24 no.5
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    • pp.856-862
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    • 1987
  • An local adaptive image segmentatin algorithm for local feature detection and effective clustering of unimodal histogram shape are proposed. Local adaptive difference image and its histogram are obtained from the input image. The parameters are derived from the histogram and used for the segmentation based on relaxatin process. The results showed effective region segmentation and good noise cleaning for the ocular fundus fluorescein angiogram which has low contrast and unimodal histogram.

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Improvement of Stereo Depth Image and Object Segmentation for Household Robot Applications (가정용 로봇 응용 시스템을 위한 스테레오 영상 개선과 객체분할)

  • Lee, Byoung-Moo;Han, Dong-Il
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.209-210
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    • 2007
  • Obtained disparity map from the stereo camera by using the several stereo matching algorithms carries lots of noise because of various causes. In our approach, mode filtering and noise elimination technique using the histogram and projection-based region merging methods are adopted for improving the quality of disparity map and image segmentation. The proposed algorithms are implemented in VHDL and the real-time experimentation shows the accurately divided objects.

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Automatic Detection of Initial Positions for Mass Segmentation in Digital Mammograms (디지털 마모그램에서 Mass형 유방암 분할을 위한 초기 위치 자동 검출)

  • Lee, Bong-Ryul;Lee, Myeong-Jin
    • Journal of Korea Multimedia Society
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    • v.13 no.5
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    • pp.702-709
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    • 2010
  • The performance of mass segmentation is greatly influenced by an initial position of a mass. Some researchers performed mass segmentation with the initial position of a mass given by radiologists. The purpose of our research is to find the initial position for mass segmentation and to notify the segmented mass to radiologists without any additional information on mammograms. The proposed system consists of breast segmentation by region growing and opening operations, decision of an initial seed with characteristics of masses, and mass segmentation by a level set segmentation. A seed for mass segmentation is set based on mass scoring measure calculated by block-based variances and masked information in a sub-sampled mammogram. We used a DDSM database to evaluate the system. The accuracy of mass detection is 78% sensitivity at 4 FP/image, and it reached 92% if multiple views for masses were considered.

A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

  • Jingxiao Liu;Yujie Wei ;Bingqing Chen;Hae Young Noh
    • Smart Structures and Systems
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    • v.31 no.4
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    • pp.325-334
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    • 2023
  • Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

Multi-level Thresholding using Fuzzy Clustering Algorithm in Local Entropy-based Transition Region (지역적 엔트로피 기반 전이 영역에서 퍼지 클러스터링 알고리즘을 이용한 Multi-Level Thresholding)

  • Oh, Jun-Taek;Kim, Bo-Ram;Kim, Wook-Hyun
    • The KIPS Transactions:PartB
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    • v.12B no.5 s.101
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    • pp.587-594
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    • 2005
  • This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.

Auto Correction Technique of Photography Composition Using ROI Extraction Method (ROI 추출을 통한 사진 구도 자동 보정 기법)

  • Ha, Ho-Saeng;Park, Dae-Hyun;Kim, Yoon
    • Journal of Information Technology and Architecture
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    • v.10 no.1
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    • pp.113-122
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    • 2013
  • In this paper, we propose the method that automatically corrects the composition of a picture stylishly as well as reliably by cropping pictures based on the Rule of Thirds. The region of interest (ROI) is extracted from a picture by applying the Saliency Map and the Image Segmentation technology, the composition of the photo is amended based on this area to satisfy the Rule of Thirds. In addition, since the face region of the person is added to ROI by the Face Detection technique and the composition is amended by the various scenario according to ROI, the little more natural picture is acquired. The experimental result shows that the photo of the corrected composition was naturally amended compared with the original photo.

Extraction of Tongue Region using Graph and Geometric Information (그래프 및 기하 정보를 이용한 설진 영역 추출)

  • Kim, Keun-Ho;Lee, Jeon;Choi, Eun-Ji;Ryu, Hyun-Hee;Kim, Jong-Yeol
    • 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.