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模式识别系列讲座
Lecture Series in Pattern Recognition

题   目（TITLE）：A Theory of Invariant Recognition
讲座人（SPEAKER）:  Prof. Tomaso Poggio；CBCL, McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
主 持人 (CHAIR)： Prof. Chenglin Liu
时    间 (TIME)：10:00AM, Nov. 9 (Friday), 2012
地    点 (VENUE)： The Second Meeting Room, 13th floor（中国科学院自动化研究所 模式识别国家重点实验室）
报告摘要（ABSTRACT）：
I conjecture that the sample complexity of object recognition is mostly due to geometric image transformations and that a main goal of the ventral stream in primate visual cortex is to learn-and-discount image transformations. The theory predicts that the size of the receptive fields determines which transformations are learned during development; that the transformation represented in each area determines the tuning of the neurons in the area; and that class-specific transformations are learned and represented at the top of the ventral stream hierarchy. If the theory were true, the ventral system would be a mirror of the symmetry properties of motions in the physical world.

报告人简介（BIOGRAPHY）：
Tomaso A. Poggio, is the Eugene McDermo7 Professor in the Dept. of Brain & Cognitive Sciences at MIT and a member of both the Computer Science and Artificial Intelligence Laboratory and of the McGovern Institute. He is an honorary member of the Neuroscience Research Program, a member of the American Academy of Arts and Sciences, a Founding Fellow of AAAI, a founding member of the McGovern Institute for Brain Research. Among other honors he received the Laurea Honoris Causa from the University of Pavia for the Volta Bicentennial, the 2003 Gabor Award, the Okawa Prize 2009, and the AAAS Fellowship. He is one of the most cited computational scientists with contributions ranging from the biophysical and behavioral studies of the visual system to the computational analyses of vision and learning in humans and machines. With W. Reichardt he characterized quantitatively the visuo--motor control system in the fly. With D. Marr, he introduced the seminal idea of levels of analysis in computational neuroscience. He introduced regularization as a mathematical framework to approach the ill-posed problems of vision and the key problem of learning from data. In the last decade he has developed an influential quantitative model of visual recognition in the visual cortex. The
citation for the recent 2009 Okawa prize mentions his “…outstanding contributions to the establishment of computational neuroscience, and pioneering researches ranging from the biophysical and behavioral studies of the visual system to the computational analysis of vision and learning in humans and machines.”
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