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Animat Vision: Active Vision in Artificial Animals

Animat Vision: Active Vision in Artificial Animals. by Demetri Terzopoulos and Tamer F. Rabie. Animat Vision. What’s an animat? - computational models of real animals situated in their natural habitats

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Animat Vision: Active Vision in Artificial Animals

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  1. Animat Vision: Active Vision in Artificial Animals by Demetri Terzopoulosand Tamer F. Rabie

  2. Animat Vision • What’s an animat? - computational models of real animals situated in their natural habitats • Animate vision - a paradigm which prescribes the use of artificial animals as autonomous virtual robots for active vision research

  3. Fish Animat • Challenge - to synthesize an active vision system for the fish animat, based solely on virtual retinal image analysis. • Binocular perspective projection of the 3D world onto the animat’s 2D retinas. • Use fish because they have simple goals, can swim in environment, rely on vision and recognition.

  4. Hardware vs. Software Approach • Hardware approach: - Can’t model the complexity of natural animals - Expensive • Software approach: - Can slow down the “cosmic clock” - The quantitative photometric, geometric, and dynamic information needed to render the virtual world is available explicitly

  5. Previous Related Work • A point marker on a 2D grid world • 2D cockroaches • Kinematic dog • Animats using “perceptual oracles”

  6. Qualities of Fish Animat • Motor system • Perception system • Behavior system • Form and Appearance

  7. Motor System • Comprises the fish biomechanical model, including muscle actuators and a set of motor controllers (MCs)

  8. Perception System • Model limitations as well as abilities • Perceptual attention mechanism - allows animat to act in a task-specific way • Perceptual oracles vs. animat vision

  9. Behavior System • Mediates between the perception system and the motor system of the fish

  10. Form and Appearance

  11. Eyes and Retinal Imaging

  12. Active Vision System • Must stabilize in environment • Must foveate on target

  13. Locating a Target • Use Color Histogram Intersection • Most obvious algorithm is to compare the color distribution of the target with color distributions found on the retinal image

  14. Problem with obvious solution • Only works if scale of target is similar to the scale of the image. • Works poorly if object is far away • Works poorly if object is semi-occluded.

  15. Locating Targets: Second Try • Iterate over scaled versions of the image and take the average. • Good: Generally converges after 2-4 iterations. • Bad: Leads to false alarms if model is overly scaled.

  16. Locating Targets: Third Try • Like before, but use a weighted average to place more importance on colors that are specific to the model. • In their experiments, usually converged to P>0.8 or P<0.2 within a few iterations.

  17. Navigation • Once targets are located and can be tracked, navigation is trivial. • When left-right vision angles deviate by more than 30 degrees from center, tell body to turn left/right. • When up-down vision angles deviate by more than 5 degrees, tell body to push up/down.

  18. Pursuing Targets in Motion • How does this fish perform in pursuit of another virtual fish? • Ran an experiment and plotted gaze angles. • Performed well, was not distracted by fake targets.

  19. Plot of Pursuit

  20. Picture from Pursuit 1/2

  21. Picture from Pursuit 2/2

  22. Conclusions, Looking Forward... • Achieved goal of implementing a software-based artificial life simulation. • In the future, would like to develop a better active vision algorithm more suited to real fish. • Model can be made realistic enough to use resulting data to form theories about animals and robotic situated agents.

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