21 July 2017 A multiscale Markov random field model in wavelet domain for image segmentation
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042026 (2017) https://doi.org/10.1117/12.2281925
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
The human vision system has abilities for feature detection, learning and selective attention with some properties of hierarchy and bidirectional connection in the form of neural population. In this paper, a multiscale Markov random field model in the wavelet domain is proposed by mimicking some image processing functions of vision system. For an input scene, our model provides its sparse representations using wavelet transforms and extracts its topological organization using MRF. In addition, the hierarchy property of vision system is simulated using a pyramid framework in our model. There are two information flows in our model, i.e., a bottom-up procedure to extract input features and a top-down procedure to provide feedback controls. The two procedures are controlled simply by two pyramidal parameters, and some Gestalt laws are also integrated implicitly. Equipped with such biological inspired properties, our model can be used to accomplish different image segmentation tasks, such as edge detection and region segmentation.
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Peng Dai, Yu Cheng, Shengchun Wang, Xinyu Du, Dan Wu, "A multiscale Markov random field model in wavelet domain for image segmentation", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042026 (21 July 2017); doi: 10.1117/12.2281925; https://doi.org/10.1117/12.2281925
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