Markov random field modeling in computer vision
Markov random field modeling in computer vision
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Recovering Surface Layout from an Image
International Journal of Computer Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Labeling irregular graphs with belief propagation
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel labeling using MRF. But the local interaction in MRF is limited to pixel label comparison. We propose a new localized superpixel Markov random field (SMRF) model to incorporate local data interaction in unsupervised parameter learning. The advantages of the new model include computational efficiency by using superpixel structure and its ability to integrate local knowledge in the learning process. Quantitative evaluation and visual effects show that the new model achieves not only better segmentation accuracy but also lower computational cost than the baseline pixel based model.