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Browse All Reviews > Computing Methodologies (I) > Image Processing And Computer Vision (I.4) > Segmentation (I.4.6) > Pixel Classification (I.4.6...)
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1-4 of 4
Reviews about "Pixel Classification (I.4.6...)":
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Motion segmentation using Markov random field model for accurate moving object segmentation
Jung C., Kim J. Ubiquitous information management and communication (Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, Suwon, Korea, 414-418, 2008. Type: Proceedings
In this paper, Jung and Kim employ motion segmentation, followed by Markov random field (MRF) silhouette refinement....
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Sep 16 2008 |
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Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
Wang J., Li J., Gray R., Wiederhold G. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(1): 85-90, 2001. Type: Article
This paper describes an algorithm for segmenting a low depth of field (DOF) image into two regions: foreground and background. The underlying assumption is that an input image consists of a focused foreground and a blurred background, ...
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May 1 2001 |
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Image Thresholding by Indicator Kriging
Oh W., Lindquist W. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(7): 590-602, 1999. Type: Article
A technique using thresholding and indicator kriging to segment an image into two classes is presented. Indicator kriging is a linear regression estimator that minimizes the variance of the prediction error and systematically sets the ...
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Nov 1 1999 |
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Parallel and sequential transformations on digital images
Yamashita M. Pattern Recognition 18(1): 31-41, 1985. Type: Article
This paper presents some interesting results about the nature of sequential and parallel algorithms based on fixed neighborhoods. The exact type of algorithms investigated is one which invokes the same subroutine at each pixel, conside...
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Apr 1 1986 |
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