Siwei Lyu Computer Science Department University at Albany, SUNY, USA Stefan Roth Computer Science Department Technische Universität Darmstadt, Germany tutorial web page: http://www.gris.informatik.tu-darmstadt.de/teaching/iccv2009/index.en.htm Modeling Natural Image Statistics for Computer Vision Part III - MRF Models in the Wavelet Domain Lecturer: Siwei Lyu
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Siwei LyuComputer Science Department
University at Albany, SUNY, USA
Stefan RothComputer Science Department
Technische Universität Darmstadt, Germany
tutorial web page: http://www.gris.informatik.tu-darmstadt.de/teaching/iccv2009/index.en.htm
Modeling Natural ImageStatistics for Computer Vision
Part III - MRF Models in the Wavelet DomainLecturer: Siwei Lyu
■ a unified statistical image model for texture and structures
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09/27/2009Siwei Lyu and Stefan Roth
related challenges■ time -- natural videos■ 3D -- natural range images■ motion -- natural optical flows■ chromatics -- natural color images■ lighting - natural illuminations■ properties of specific image class
• medical images [Pineda et.al., SPIE 2008]• satellite images [Jager & Hellwich, IGRASS 2005]• face images [Liu et.al., IJCV 2004]• human bodies [Norouzi, CVPR 2009]
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09/27/2009Siwei Lyu and Stefan Roth
big question marks■ what are natural images, anyway?
■ white noises are “natural” as they are the result of cosmic radiations
■ naturalness is in the eyes of the beholders
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09/27/2009Siwei Lyu and Stefan Roth
big question marks■ what are natural images, anyway?
■ white noises are “natural” as they are the result of cosmic radiations
■ naturalness is in the eyes of the beholders
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“unnatural” to a prehistoric human
09/27/2009Siwei Lyu and Stefan Roth18
natural!
image!
statistics
math
statisticsbiology
computer!
science
image!
processing
machine!
learning
computer!
vision
optimization
perception
neuro-!
science
signal!
processing
09/27/2009Siwei Lyu and Stefan Roth19
future directions
■ comprehensive model capturing all known statistical properties of natural images
■ efficient algorithms for learning and inference■ tighter connection to mid and high level computer
vision• principled framework to find effective feature types based on
image statistics• and many more … …
09/27/2009Siwei Lyu and Stefan Roth
resources■ D. L. Ruderman. The statistics of natural images. Network: Computation in
Neural Systems, 5:517–548, 1996. (good introduction)■ E. P. Simoncelli and B. Olshausen. Natural image statistics and neural
representation. Annual Review of Neuroscience, 24:1193–1216, 2001. (neural science perspective)
■ S.-C. Zhu. Statistical modeling and conceptualization of visual patterns. IEEE Trans PAMI, 25(6), 2003. (computer vision perspective)
■ A. Srivastava, A. B. Lee, E. P. Simoncelli, and S.-C. Zhu. On advances in statistical modeling of natural images. J. Math. Imaging and Vision, 18(1):17–33, 2003. (mathematical perspective)
■ E. P. Simoncelli. Statistical modeling of photographic images. In Handbook of Image and Video Processing, 431–441. Academic Press, 2005. (signal processing perspective)
■ A. Hyvärinen, J. Hurri, and P. O. Hoyer. Natural Image Statistics: A probabilistic approach to early computational vision. Springer, 2009. (statistical modeling and recent accounts)