Abstract
This paper proposes a Markov Random Field model for image segmentation based on statistical characteristics of contours. Different from previous approaches, we use Gestalt Laws of Perceptual Organization as natural constraints for segmentation by integrating contour orientations into segmentation labels. The basic framework of our model consists of three modules: foreground/backgraound separation, attentive selection and information integration. This model can be realized for both automatic and semiautomatic image segmentations. Our algorithm achieves smooth segmentation boundaries and outperforms other popular algorithms.
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Ren, Y., Tang, H., Wei, H. (2011). A Markov Random Field Model for Image Segmentation Based on Gestalt Laws. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_66
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DOI: https://doi.org/10.1007/978-3-642-24965-5_66
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