1. 1 Computational Vision CSCI 363, Fall 2012 Lecture 35 Perceptual Organization II

2. 2 Project Presentations You will be graded on the following: Content Presentation Style Structure

3. 3 Content You have some latitude as to what you present for your project, but you must include the following: Introduction -- What is your topic/question? Why did you choose it? Background -- Describe the problem you are addressing. What are the important questions about this problem that need to be addressed? What is the basic theory underlying the problem? Main result -- Describe the main experiments or theories that you researched. What is the scientific data that supports the theories? How do the results relate to the main problem? Conclusion -- What can we conclude about the problem?

4. 4 Presentation Style How you present is at least (if not more) important than what you present. You need to focus on: Making eye contact with the audience Speaking clearly and audibly Keeping a calm demeanor (at least appear to be calm) Pacing the talk well Giving the talk while standing (Don't sit!)

5. 5 Visual Interpolation The visual system is very good at filling in gaps in the visual information available. This filling-in is known as interpolation. Evidence: Blind spot: We are unaware of our blind spot, because the gap in our visual field is filled in. Stroke patients: Some stroke patients lose vision in large portions of the visual field, but are unaware of this loss. The extent of the gap can be determined with careful clinical tests. Peripheral vision: We only have finely detailed vision in the center of our visual field. The peripheral image is quite blurry. Our visual system "fills in" details so we think we have a detailed view of the entire scene.

6. 6 Occlusion In a typical real scene, many objects are at least partially blocked, or occluded, by other objects. We have no problem perceiving the entire object. Our visual system automatically fills in the missing part. This is known as visual completion or amodal completion. Example: We perceive this as a circle that is partially occluded by a square.

7. 7 Rules for completion How does the visual system know what the completed shapes will look like? a)Familiarity: Familiarity is not required, but it helps.

8. 8 Other Possible Rules Gestaltists believed that a figure could have a measure of "goodness" or "pragnanz". Problem: How do you measure goodness? Example: The more axes of symmetry, the better the figure. Counter example: Don't see hexagon in figure below. Kellman and Shipley developed a set of rules for relating pairs of discontinuous edges to one another for use in visual completion. The theory is a bit more quantitative than the others, but it still has difficulties explaining all the details of visual completion.

9. 9 Illusory Contours The Kanisza Triangle Certain configurations cause us to see lines that are not actually present in the image.

10. 10 Relation to Visual Completion We only see illusory contours when some pieces of the image appear to be occluded. The visual system "completes" the occluded regions.

11. 11 Physiology Von der Heyt found cells in V2 that respond to illusory contours.

12. 12 Responses of cells

13. 13 Model for V2 response to illusory contours. This model makes use of end-stopped cells lined up along the contour.

14. 14 Fun with Size Constancy The Ponzo illusion The Shepard illusion

15. 15 Organizing the Scene Once you have detected oriented lines and determined their depth, color and motion, you must determine which parts of the image go with which other parts. Stages of perceptual organization: 1)Image Segmentation: Divide the image into parts. 2)Grouping: Group together parts that belong together. 3)Parsing: Locate distinct pieces of the segmented regions.

16. 16 Segmentation A region = A bounded 2-D area that is a spatial subset of the image. How do we define these: 1)Uniform connectedness: Find regions of uniform image properties (luminance, color, texture, etc.) 3 regions 2 regions3 regions How do segment more complex images?

17. 17 Boundary Based segmentation Use an edge detection algorithm to find closed contours. When the edges form a closed contour, this divides the image into 2 regions: a bounded inside and an outside region. Marr-Hildreth edge detection (zero crossings): Guaranteed to form closed contours. Other methods may or may not generate closed contours.

18. 18 Problems with edges One problem with edge-based methods is that there may be many spurious edges in the image from shadows and highlights. There needs to be a way to find connected surfaces in spite of lighting conditions. A second problem with edge based methods occurs when one object partially occludes another. We still tend to perceive the whole occluded object.

19. 19 Region based approaches Region based approaches identify regions by finding sets of pixels that are most similar within a region and most dissimilar between regions. Note that the outline of the regions found is reasonably true to what we perceive. The edge detection algorithm (Canny) does not do as well. Edges Regions

20. 20 Evidence for Edges The human visual system probably makes use of edges for image segmentation. Evidence: If you stabilize an edge on the retina, it disappears. Image segmentation also disappears. Experiment: Red circle in green annulus. Stabilize inner edge, but not outer edge. Red circle disappears!