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Volumn 27, Issue 9, 2009, Pages 1326-1335

A spatially distributed model for foreground segmentation

Author keywords

Background model; Foreground segmentation; Mixture of Gaussians; Spatial coherence

Indexed keywords

DATA COMPRESSION; HIDDEN MARKOV MODELS; IMAGE SEGMENTATION; INDUCTIVELY COUPLED PLASMA; LEARNING ALGORITHMS; MIXTURES; VIDEO RECORDING;

EID: 67349233839     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2008.12.001     Document Type: Article
Times cited : (28)

References (28)
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  • 2
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  • 6
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    • Motion-based object detection and tracking in color image sequences
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    • Heisele, B.1
  • 7
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    • A pixel-wise object tracking algorithm with target and background sample
    • Hong Kong, China
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  • 17
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    • Evaluation of global image thresholding for change detection
    • Rosin P., and Ioannidis E. Evaluation of global image thresholding for change detection. Pattern Recognition Letters 24 14 (2003) 2345-2356
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    • Rosin, P.1    Ioannidis, E.2
  • 18
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    • Smooth foreground-background segmentation for video processing
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    • K. Schindler, H. Wang, Smooth foreground-background segmentation for video processing, in: Proc. of Asian Conference on Computer Vision, vol. 2, Hyderabad, India, 2006, pp. 581-590.
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  • 22
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    • Moving object segmentation by background subtraction and temporal analysis
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  • 26
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    • A dynamic hidden Markov random field model for foreground and shadow segmentation
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    • Y. Wang, K. Loe, T. Tan, J. Wu, A dynamic hidden Markov random field model for foreground and shadow segmentation, in: Proc. of IEEE Workshop on Motion and Video Computing, Breckenridge, CO, USA, 2005, pp. 474-480.
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    • Wang, Y.1    Loe, K.2    Tan, T.3    Wu, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.