1. Rules by which the brain segments an object from the background: Evaluation of the Gabor model of simple cell receptive fields Beth Tuck Hanover College 4/13/2007

2. Background Brain must “make sense” of massive amounts of visual information to generate holistic picture of an object Brain must “make sense” of massive amounts of visual information to generate holistic picture of an object e.g. e.g.

3. Task of Vision

8. Figure-Ground Perception World is divided into (1) figure being inspected and (2) background World is divided into (1) figure being inspected and (2) background

9. Segmentation The process of separating a figure/object from background (Marr, 1982) The process of separating a figure/object from background (Marr, 1982) Outline needed to segment Outline needed to segment Image from Marr (1982)

10. Simple Cell Receptive Fields Simple cells (orientation specific) may assist in segmentation process Simple cells (orientation specific) may assist in segmentation process Krantz (1994). Sensation & Perception Receptive Field Tutorial : http://psych.hanover.edu/krantz/receptive/

11. Research Question How do simple cells (modeled under the Gabor function) allow the brain to segment visual information? How do simple cells (modeled under the Gabor function) allow the brain to segment visual information? –What are the “Rules” by which modeled cells segment visual information

12. Step 1: Develop Model Feed stimulus into program Feed stimulus into program Program presents stimulus to cells @ 18 orientations (rotates by 10 o increments) Program presents stimulus to cells @ 18 orientations (rotates by 10 o increments) Program records output @ each orientation Program records output @ each orientation

13. Orientation and Output of Model

14. Step 2: Bar Stimulus Test Feed bars of various widths Feed bars of various widths Attempt to determine rules by which these model cells allow segmentation Attempt to determine rules by which these model cells allow segmentation

15. Example of Generation of Output

16. Rule 1: Orientation of Model Cell with Greatest (Positive or Negative) Output At each location: find modeled cell with maximum output At each location: find modeled cell with maximum output Plot orientation of that sensitivity Plot orientation of that sensitivity –Most positive – draw in white –Most negative – draw in red

17. Most Positive Most Negative

18. Rule 2: Greatest Output & Limited Range of Response At many locations, the responses of all orientations are both: At many locations, the responses of all orientations are both: –Relatively strong –All approximately the same value

19. Limited Range of Model Output In this case, model cells don’t seem to indicate orientation In this case, model cells don’t seem to indicate orientation The square at the location is filled with an intensity to match the mean output The square at the location is filled with an intensity to match the mean output White for positive; red for negative White for positive; red for negative

20. Most Positive Most Negative + Limited Range Activity

21. Rule 2 Close-up

22. So far each location processed independent of surrounding locations So far each location processed independent of surrounding locations What if output also depends upon adjacent responses? What if output also depends upon adjacent responses? E.g. color where color depends not just on response of one cone but all three E.g. color where color depends not just on response of one cone but all three –Red alone gives red –Red and green gives yellow

23. Rule 3: Local Maximum Plot Orientation of this Maximum

24. Rule 3: Local Minimum Plot Orientation of this Minimum

25. Local Maxima Local Minima + Limited Range Activity

26. Conclusions Local Maxima/Minima + Limited Range seems to most accurately recreate stimulus Local Maxima/Minima + Limited Range seems to most accurately recreate stimulus Hermann-Hering grid Hermann-Hering grid –Fovea – does not segment circle –Periphery – segments circle Verify with human subjects Verify with human subjects