Fcv appli science_perona

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Fcv appli science_perona

  1. 1. Applications to Science Pietro Perona California Institute of Technology NSF Workshop - Frontiers in Vision Cambridge, 23 Aug 2011Friday, August 26, 2011
  2. 2. Goals • A few examples • Implications for machine vision • Lessons learned • NSF’s roleFriday, August 26, 2011
  3. 3. Plan • Intro (5’) • Sketch of a few success stories (50’) • Discussion (10’)Friday, August 26, 2011
  4. 4. ‘Lunging’ (view from top)Friday, August 26, 2011
  5. 5. Why measure behaviorFriday, August 26, 2011
  6. 6. Why measure behavior • Genes <<>> Brains <<>> BehaviorFriday, August 26, 2011
  7. 7. Why measure behavior • Genes <<>> Brains <<>> Behavior • EthologyFriday, August 26, 2011
  8. 8. Why measure behavior • Genes <<>> Brains <<>> Behavior • Ethology • What is behavior?Friday, August 26, 2011
  9. 9. Fly behavior (as we understand it today) Adapted from: Kravitz et al. PNAS April 16, 2002 vol. 99 no. 8 5664–5668Friday, August 26, 2011
  10. 10. Friday, August 26, 2011
  11. 11. Friday, August 26, 2011
  12. 12. Detection performance [Dankert et al., Nature Methods, April 2009]Friday, August 26, 2011
  13. 13. Phenotyping [Dankert et al., Nature Methods, 2009]Friday, August 26, 2011
  14. 14. Ethograms [Dankert et al., Nature Methods, April 2009]Friday, August 26, 2011
  15. 15. Perception PSYCHOLOGY interaction, cooperation, competition plans, goals, behavior, relationships ... pose, movemes, actions, activities, objects, scenes SENSORY images, trajectories WorldFriday, August 26, 2011
  16. 16. Action Perception PSYCHOLOGY interaction, cooperation, PLANNING group-level goals and plans competition SOCIAL NETWORK THEORY OF SOCIOLOGY INDIVIDUAL plans, goals, behavior, individual goals and plans relationships ... PREFRONTAL CORTEX THEORY OF PSYCHOLOGY pose, movemes, actions, MOTOR motor programs activities, objects, scenes SENSORY MOTOR CORTEX RECOGNITION sensor-based control images, trajectories SPINAL CORD IMAGING,TRACKING WorldFriday, August 26, 2011
  17. 17. Lessons learned • Image deluge in science • Doing better than the scientists • Payoffs in science, not in MV (short term) ‣ Must work as scientist ‣ Students must be interested in science too ‣ Publish in unfamiliar venues ‣ CV publications are suspicious • Benefit to MV: new challenges and datasets • Benefit to PI: fun, learningFriday, August 26, 2011
  18. 18. Basic research needed • Tracking, detection and identification • Parts and pose • Hierarchical models (for time series) • Unsupervised discovery of categories • Weakly supervised learningFriday, August 26, 2011

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