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A Probabilistic Model of the Visual System

A Probabilistic Model of the Visual System. Siddhartha Kasivajhula. PSYCH 221 Final Presentation Mar 18, 2008. A Probabilistic Model of the Visual System. A model that describes: How visual information is observed How the brain interprets the scene How objects are recognized

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A Probabilistic Model of the Visual System

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  1. A Probabilistic Model of the Visual System Siddhartha Kasivajhula PSYCH 221 Final Presentation Mar 18, 2008

  2. A Probabilistic Model of the Visual System • A model that describes: • How visual information is observed • How the brain interprets the scene • How objects are recognized • I will use this model to explain the Bruner & Potter Experiment

  3. Motivation • A walk around Lake Lagunita on a cold dark night… • Eyes used for “falsification” of visual information • The eyes provide a poor image to the brain

  4. Simulated retina video • Insert video here. • Video credit: Laurent Itti, Christof Koch, Jan 1998. • Obtained from Prof. Daniel C. Richardson, UCSC

  5. Model architecture PRE-PROCESSING Create prior probability distributions for objects in the scene VISUAL INFORMATION Obtain evidence about the scene from the eyes POST-PROCESSING Create new distributions over objects in scene based on evidence

  6. The Model: Representation • Objects are recognized based on some features that they exhibit (eg. Texture, geometry) • People recognize familiar objects more quickly[Wang, 1990] • implies that prior knowledge of objects is used in visual interpretation

  7. The Model: Representation • Features are represented as vectors • represents feature 4 being present, and all other features absent • An observation is represented as a set of feature vectors: represents that features 2 and 4 were observed

  8. The Model: Observation • The process of observation is modeled as a Hidden Markov Model (HMM). S0 S1 S2 S3 . . . O1 O2 O3 t = 0 t = 1 t = 2 t = 3 . . .

  9. The Model: Interpretation • The process of interpretation is modeled as a noisy-or Bayes Net classifier Ψ0 Ψ1 Ψ2 Ψ3 . . . f1 f2 f3 f4 f5 f6 . . .

  10. The Bruner & Potter Experiment • A defocused image is brought into focus over a period of time (60 seconds*?) • Subjects who start looking at the image earlier identify it correctly after those who start looking at the image later

  11. The Bruner & Potter Experiment Observation • The results of the experiment can be described as: • Each ot-1 is a less information rich, more noisy version of the subsequent ot Interpretation

  12. Future Directions • Explain object locality within image • Features are associated with a region of the scene • Object tracking • This can be done once feature locality is modeled • Refine model, incorporate more complexity

  13. Summary • A probabilistic model of the visual system was specified • Representation • The visual scene is represented by features • Known objects are stored as feature weights • Observation • Is modeled as a Hidden Markov Model • Interpretation • The scene is interpreted using a noisy-or Bayes classifier • The Bruner & Potter results were reinterpreted and explained using this model

  14. Appendix: Experiment to establish ‘texture’ as the dominant depth feature

  15. References • L. Wang, J. Ross, “Interactions of neural networks: Models for distraction and concentration”, 1990. • J. Bruner, M. Potter, “Interference in Visual Recognition”, 1964. • P.Churchland, et. al, “A Critique of Pure Vision”

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