1. Animat Vision: Active Vision in Artificial Animals by Demetri Terzopoulos and 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.