A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Nonlinear Approach for Face Sketch Synthesis and Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hallucinating Faces: TensorPatch Super-Resolution and Coupled Residue Compensation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face transfer with multilinear models
ACM SIGGRAPH 2005 Papers
IEEE Transactions on Circuits and Systems for Video Technology
A new approach for face recognition by sketches in photos
Signal Processing
Image-based facial sketch-to-photo synthesis via online coupled dictionary learning
Information Sciences: an International Journal
Heterogeneous image transformation
Pattern Recognition Letters
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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This paper develops a statistical inference approach, Bayesian Tensor Inference, for style transformation between photo images and sketch images of human faces. Motivated by the rationale that image appearance is determined by two cooperative factors: image content and image style, we first model the interaction between these factors through learning a patch-based tensor model. Second, by introducing a common variation space, we capture the inherent connection between photo patch space and sketch patch space, thus building bidirectional mapping/inferring between the two spaces. Subsequently, we formulate a Bayesian approach accounting for the statistical inference from sketches to their corresponding photos in terms of the learned tensor model. Comparative experiments are conducted to contrast the proposed method with state-of-the-art algorithms for facial sketch synthesis in a novel face hallucination scenario: sketch-based facial photo hallucination. The encouraging results obtained convincingly validate the effectiveness of our method.