Transcript
Applications to Science
Pietro PeronaCalifornia Institute of Technology
NSF Workshop - Frontiers in VisionCambridge, 23 Aug 2011
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Goals
• A few examples
• Implications for machine vision
• Lessons learned
• NSF’s role
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Plan
• Intro (5’)
• Sketch of a few success stories (50’)
• Discussion (10’)
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‘Lunging’ (view from top)
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Why measure behavior
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Why measure behavior
• Genes <<>> Brains <<>> Behavior
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Why measure behavior
• Genes <<>> Brains <<>> Behavior
• Ethology
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Why measure behavior
• Genes <<>> Brains <<>> Behavior
• Ethology
• What is behavior?
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Adapted from:Kravitz et al.PNAS April 16, 2002 vol. 99 no. 85664–5668
Fly behavior(as we understand it today)
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Detection performance
[Dankert et al., Nature Methods, April 2009]
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Phenotyping
[Dankert et al., Nature Methods, 2009]Friday, August 26, 2011
Ethograms
[Dankert et al., Nature Methods, April 2009]Friday, August 26, 2011
images, trajectories
pose, movemes, actions, activities, objects, scenes
plans, goals, behavior, relationships ...
SENSO
RYPSY
CH
OLO
GY
Perception
World
interaction, cooperation, competition
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group-level goals and plans
individual goals and plans
motor programs
sensor-based control
IMAGING,TRACKING
RECOGNITION
THEORY OF PSYCHOLOGY
SOCIAL NETWORK
PREFRONTAL CORTEX
MOTOR CORTEX
SPINAL CORD
MO
TOR
PLA
NN
ING
Action
INDIVIDUAL
THEORY OF SOCIOLOGY
images, trajectories
pose, movemes, actions, activities, objects, scenes
plans, goals, behavior, relationships ...
SENSO
RYPSY
CH
OLO
GY
Perception
World
interaction, cooperation, competition
Friday, August 26, 2011
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, learning
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Basic research needed
• Tracking, detection and identification
• Parts and pose
• Hierarchical models (for time series)
• Unsupervised discovery of categories
• Weakly supervised learning
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