• 제목/요약/키워드: Connected Component Labeling (CCL)

검색결과 8건 처리시간 0.021초

Parallel Connected Component Labeling Based on the Selective Four Directional Label Search Using CUDA

  • Soh, Young-Sung;Hong, Jung-Woo
    • 융합신호처리학회논문지
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    • 제16권3호
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    • pp.83-89
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    • 2015
  • Connected component labeling (CCL) is a mandatory step in image segmentation where objects are extracted and uniquely labeled. CCL is a computationally expensive operation and thus is often done in parallel processing framework to reduce execution time. Various parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method, modified 8 directional label selection (M8DLS) method, HYBRID1 method, and HYBRID2 method. Soh et al. showed that HYBRID2 outperforms the others and is the best so far. In this paper we propose a new hybrid parallel CCL algorithm termed as HYBRID3 that combines selective four directional label search (S4DLS) with label backtracking (LB). We show that the average percentage speedup of the proposed over M8DLS is around 60% more than that of HYBRID2 over M8DLS for various kinds of images.

An Improved Hybrid Approach to Parallel Connected Component Labeling using CUDA

  • Soh, Young-Sung;Ashraf, Hadi;Kim, In-Taek
    • 융합신호처리학회논문지
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    • 제16권1호
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    • pp.1-8
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    • 2015
  • In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

대규모 레이더 신호 데이터의 실시간 분석을 위한 GPU 기반 객체 추출 기법 (GPU-based Object Extraction for Real-time Analysis of Large-scale Radar Signal)

  • 강영민
    • 한국멀티미디어학회논문지
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    • 제19권8호
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    • pp.1297-1309
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    • 2016
  • In this paper, an efficient connected component labeling (CCL) method was proposed. The proposed method is based on GPU parallelism. The CCL is very important in various applications where images are analysed. However, the label of each pixel is dependent on the connectivity of adjacent pixels so that it is not very easy to be parallelized. In this paper, a GPU-based parallel CCL techniques were proposed and applied to the analysis of radar signal. Since the radar signals contains complex and large data, the efficiency of the algorithm is crucial when realtime analysis is required. The experimental results show the proposed method is efficient enough to be successfully applied to this application.

연속 영상 기반 실시간 객체 분할 (Real-Time Object Segmentation in Image Sequences)

  • 강의선;유승훈
    • 정보처리학회논문지B
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    • 제18B권4호
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    • pp.173-180
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    • 2011
  • 본 논문은 GPU(Graphics Processing Unit) 에서 CUDA(Compute Unified Device Architecture)를 사용하여 실시간으로 객체를 분할하는 방법을 소개한다. 최근에 감시 시스템, 오브젝트 추적, 모션 분석 등의 많은 응용 프로그램들은 실시간 처리가 요구된다. 이러한 단계의 선행부분인 객체 분할 기법은 기존 CPU 기반의 시스템으로는 실시간 처리에 제약이 발생한다. NVIDIA에서는 Parallel Processing for General Computation 을 위해 그래픽 하드웨어 제약을 개선한 CUDA platform을 제공하고 있다. 본 논문에서는 객체 추출 단계에 대표적인 적응적 가우시안 혼합 배경 모델링(Adaptive Gaussian Mixture Background Modeling) 알고리즘과 Classification 기법으로 사용되는 CCL (Connected Component Labeling) 알고리즘을 적용하였다. 본 논문은 2.4GHz를 갖는 Core2 Quad 프로세서와 비교하여 평가하였고 그 결과 3~4배 이상의 성능향상을 확인할 수 있었다.

3차원 깊이 정보 기반의 감시카메라 영상 분석 (Image Analysis for Surveillance Camera Based on 3D Depth Map)

  • 이수빈;서용덕
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송공학회 2012년도 하계학술대회
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    • pp.286-289
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    • 2012
  • 본 논문은 3차원 깊이 정보를 이용하여 감시카메라에서 움직이는 사람을 검출하고 추적하는 방법을 제안한다. 제안하는 방법은 GMM(Gaussian mixture model)을 이용하여 배경과 움직이는 사람을 분리한 후, 분리된 영역을 CCL(connected-component labeling)을 통하여 각각 블랍(blob) 단위로 나누고 그 블랍을 추적한다. 그 중 블랍 단위로 나누는 데 있어 두 블랍이 합쳐진 경우, 3차원 깊이 정보를 이용하여 두 블랍을 분리하는 방법을 제안한다. 실험을 통하여 제안하는 방법의 결과를 보인다.

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

  • Kang, Ho Chul;Shin, Yeong-Gil;Lee, Jeongjin
    • IEIE Transactions on Smart Processing and Computing
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    • 제3권4호
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    • pp.191-194
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    • 2014
  • 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.

피부 색상 및 아다부스트 알고리즘을 이용한 안정적 얼굴감지 (Stable Face Detection using Skin-tone and AdaBoost Algorithm)

  • 최유주;변재희
    • 한국정보과학회:학술대회논문집
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    • 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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    • pp.565-568
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    • 2008
  • 본 논문은 RGB 24bit 컬러 영상으로 전달되는 카메라 원영상에 대해 사람의 얼굴을 안정적으로 감지할 수 있는 알고리즘을 제시한다. RGB 입력영상을 HSI 기반의 컬러모델로 변환하여 피부 색상을 추출하고 그리드 영상을 기반으로 CCL (Connected-Component Labeling) 알고리즘을 적용하여 피부 블럽을 검출한 뒤, 아다부스트 알고리즘을 이용하여 얼굴 영역과 얼굴이 아닌 다른 피부 영역을 구분한다. 제안방법은 일반적으로 얼굴 감지를 위하여 폭넓게 사용되고 있는 아다부스트 알고리즘만을 적용하였을 때보다 얼굴감지 오류를 줄일 수 있다.

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A study on ITZ percolation threshold in mortar with ellipsoidal aggregate particles

  • Pan, Zichao;Wang, Dalei;Ma, Rujin;Chen, Airong
    • Computers and Concrete
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    • 제22권6호
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    • pp.551-561
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    • 2018
  • The percolation of interfacial transition zone (ITZ) in cementitious materials is of great importance to the transport properties and durability issues. This paper presents numerical simulation research on the ITZ percolation threshold of mortar specimens at meso-scale. To simulate the meso-scale model of mortar as realistically as possible, the aggregates are simplified as ellipsoids with arbitrary orientations. Major and minor aspect ratios are defined to represent the global shape characteristics of aggregates. Some algorithms such as the burning algorithm, Dijkstra's algorithm and Connected-Component Labeling (CCL) algorithm are adopted for identification of connected ITZ clusters and percolation detection. The effects of gradation and aspect ratios of aggregates on ITZ percolation threshold are quantitatively studied. The results show that (1) the ITZ percolation threshold is mainly affected by the specific surface area (SSA) of aggregates and shows a global decreasing tendency with an increasing SSA; (2) elongated ellipsoidal particles can effectively bridge isolated ITZ clusters and thus lower the ITZ percolation threshold; (3) as ITZ volume fraction increases, the bridging effect of elongated particles will be less significant, and has only a minor effect on ITZ percolation threshold; (4) it is the ITZ connectivity that is essentially responsible for ITZ percolation threshold, while other factors such as SSA and ITZ volume fraction are only the superficial reasons.