A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical methods of optimization; (2nd ed.)
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Probabilistic reasoning in intelligent systems: networks of plausible inference
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ACM SIGGRAPH 2003 Papers
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
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ACM SIGGRAPH 2007 papers
ACM SIGGRAPH Asia 2008 papers
Analysis of Building Textures for Reconstructing Partially Occluded Facades
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Discovering texture regularity as a higher-order correspondence problem
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Many man-made and natural structures consist of similar elements arranged in regular patterns. In this paper we present an unsupervised approach for discovering and reasoning on repetitive patterns of objects in a single image. We propose an unsupervised detection technique based on a voting scheme of image descriptors. We then introduce the concept of latticelets: minimal sets of arcs that generalize the connectivity of repetitive patterns. Latticelets are used for building polygonal cycles where the smallest cycles define the sought groups of repetitive elements. The proposed method can be used for pattern prediction and completion and high-level image compression. Conditional Random Fields are used as a formalism to predict the location of elements at places where they are partially occluded or detected with very low confidence. Model compression is achieved by extracting and efficiently representing the repetitive structures in the image. Our method has been tested on simulated and real data and the quantitative and qualitative result show the effectiveness of the approach.