Robust Object Recognition with Cortex-Like Mechanisms

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Authors: Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and
Tomaso Poggio, Member, IEEE

Reporter: Lê Ngọc Minh

In Computational vision course, Master of Cognitive science, Trento University

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Robust Object Recognition with Cortex-Like Mechanisms

  1. 1. Robust Object Recognition with Cortex-Like MechanismsAuthors: Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso Poggio, Member, IEEE Reporter: Lê Ngọc Minh
  2. 2. Content 1. How is it like? 2. Where does it come from? 3. What is it? 1. Performance 2. Authors contribution 3. Unsolved problems 4. Where will it go?November 28, 2012 2
  3. 3. How is it like? ● As demonstrated by Neocognitron – Youtube: http://www.youtube.com/watch? v=Qil4kmvm2SwNovember 28, 2012 3
  4. 4. Where does it come from? ● 1959, 1962: Simple and Complex cells (Torsten Wiesel and David Hubel) ● 1980: Neocognitron (Fukushima) ● 1999: HMAX ● 2005: Feedforward model of the ventral stream in primate visual cortex (Serre et. al) ● 2007: Cortex-like algorithm (Serre et. al)November 28, 2012 4
  5. 5. What is it? ● New factors in the algorithm compare to Neocognitron: – Gabor filter for simple cells (J. G. Daugman, 1985) – Max for complex cellsNovember 28, 2012 5
  6. 6. Performance● Comparable or superior to other systems in accuracy● Better than SIFT features in more general categorization tasks● Universal feature: general, independent of training Results Obtained with 1,000 C2 Features Combined examples, avoid over-fitting with SVM or GentleBoost (boost) Classifiers and problem Comparison with Existing Systems (Benchmark)November 28, 2012 8
  7. 7. Authors contributionsNovember 28, 2012 10
  8. 8. Authors contributions ● Full-fledged, working implementation of a cognitive model into computer ● Broad and multifaceted evaluation of the algorithm that demonstrates its strength ● The discovery of universal feature setNovember 28, 2012 11
  9. 9. Unsolved problems ● Theres still room for further development: parameter tuning, more complex architecture,... ● The main limitation of this approach is speed: typically, tens of seconds, depending on the size of the input image ● Many following researches try to alleviate this problem.November 28, 2012 12
  10. 10. New developments ● Exploit special hardware to achieve more efficient computation – Jim Mutch, 2010: GPUs – Al Maashri, 2011: FPGA ● Better model of simple cells: – George Azzopardi, 2012: CORFNovember 28, 2012 13
  11. 11. References● D. H. Hubel and T. N. Wiesel Receptive Fields of Single Neurones in the Cats Striate Cortex J. Physiol. pp. 574-591 (148) 1959● D. H. Hubel and T. N. Wiesel "Receptive Fields, Binocular Interaction and Functional Architecture in the Cats Visual Cortex" J. Physiol. 160 pp. 106-154 1962● K. Fukushima: "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position", Biological Cybernetics, 36[4], pp. 193-202 (April 1980).● HMAX: M. Riesenhuber and T. Poggio, Hierarchical Models of Object Recognition in Cortex, 1999● A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio, 2005)November 28, 2012 14
  12. 12. References● Robust Object Recognition with Cortex-Like Mechanisms (Thomas Serre, Lior Wolf, Stanley Bileschi, Maximilian Riesenhuber, and Tomaso Poggio, 2007)● A hardware architecture for accelerating neuromorphic vision algorithms, Al Maashri, A, 2011● CNS: a GPU-based framework for simulating cortically-organized networks Jim Mutch, Ulf Knoblich, and Tomaso Poggio, 2010● George Azzopardi, Nicolai Petkov, A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model, 2012November 28, 2012 15
  13. 13. THANK YOU!November 28, 2012 16

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