NDLI: A Fast Edge Tracking Algorithm for Image Segmentation Using a Simple Markov Random Field Model

Content Provider	IEEE Xplore Digital Library
Author	Feiyue He Zheng Tian Xiangzeng Liu Xifa Duan
Copyright Year	2012
Abstract	This paper presents an fast edge tracking algorithm for reducing the computation time of unsupervised image segmentation using a simple Markov random field model (MRF). The classical two-component MRF (CMRF) based image segmentation algorithm is time-consuming for sweeping image and repeat computing all labels at each iteration process. However, most of labels remain unchanged from an iteration to the next. So most of computations are redundant and contribute nothing to the final segmentation. The proposed algorithm works by tracking edge rather than all pixels and computing their labels at each iteration. The algorithm is simple, easy to implement but fast. Experimental results show that, compare to the image segmentation algorithm based on CMRF method, the proposed algorithms can substantially reduce the computation time but the segmentation results are comparable.
Starting Page	633
Ending Page	636
File Size	400294
Page Count	4
File Format	PDF
ISBN	9781467306898
e-ISBN	9780769546476
DOI	10.1109/ICCSEE.2012.96
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Edge tracking Image segmentation Image edge detection Computational modeling Hidden Markov models Educational institutions Markov random field Markov random fields
Content Type	Text
Resource Type	Article

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