Overview of Human and Computer Vision

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  • fovea - pit that has provides the greatest focus
    rods and cones turn light into electrochemical signals that are sent to the brain
  • cones are dedicated to bright light and colors
    3 kinds of cones
    rods are active in processing dim light
    hard to see color in dim light
  • left and right fields get processed together and in parallel
    Doesn't show path to the superior colliculus
    -- SC serves to generate quick and usually unconscious movements of the eye (saccades);
    -- often purely reflexive
    -- focuses attention onto regions of interest such as areas where a texture or color is different from its surroundings or where movement has been detected by higher areas of the brain
    LGN
    -- each LGN has 6 layers of processing, 3 for each eye;
    -- exact function is not known, but information is sent to and from V1
    Visual Cortex
    -- 5 major layers processing: orientation, position, size; _; form and shape; color; motion
    -- two paths: "where" and "what" processed in parallel
    No one completely knows how vision works in a mathematical / algorithmic sense
  • Processing involves more than just working with the image coming from the retina
    Retinal images don't tell us the difference between a hole in the ground and a shadow.
    The brain adds hints to the image for correct interpretation based on probability, past experience, and knowledge.
    Brain tweaks the image; may add additional shading or changing perceived colors to synthetically add features like depth cues
    What the brain allows you to see isn't always the image that's actually coming in. Not raw data
  • Huge area of study full of huge sub-areas of study
    Things are more objective and discrete with pixels versus fuzzy biological signals
  • Huge area.
    Deals with preprocessing an image to aid higher-level analysis
    High/low pass filtering (e.g. sharpening, blurring)
    aliasing / antialiasing
    Histogram equalization - redistributes the gray-level intensities amongst the pixels, shifting all pixels with a given intensity together (i.e. all pixels that had the same intensity before have the same intensity now, it's just a different value), thus increasing the global contrast
    cumulative distribution function - at point X, how many pixels have intensity at or below X
  • after image is prepared to be analyzed at a high level..
    Need to be able to tell difference between the foreground and background, areas / objects of interest in the image, etc.
    subproblem of a lot of different high-level problems such as
    -- object recognition/detection
    -- image classification
    discontinuities - adjacent pixel regions where local contrast exceeds some threshold
    local contrasts - define edges - define boundaries of shapes - define objects
    Canny edge detector
    problems:
    -- can create edges that don't actually exist
    -- can ignore edges that do exist
    -- no inherent way to tell if an edge is part of an object or is an object boundary; e.g. textures
  • looking for homogeneity wrt certain features (e.g. color, texture, etc);
    think of the paint bucket tool in photoshop; spread out in all directions looking for contiguous pixels that are similar
    can be used in conjunction with edge detection
  • High level image processing
    feature detection
  • Images from a system that classifies pizzas as good or bad based on the pattern and distribution of toppings
  • Disparity - thumb exercise
    Brain offers hints and cues for distance
    -- parallax - moving head, closer objects move across field of view faster; moon follows you wherever you go
    -- shadows
    -- knowledge of what things look like
    Correspondence problem
    -- some pixels don't correspond at all due to occlusions; can see more AROUND the left side with left eye, right side with right eye
    -- some areas are going to appear as different widths in the different images (e.g. slanted)
  • Smoothed image to eliminate noise
    Segmented based mostly on color and contrast.
    -- colors weren't the same due to different cameras, different lighting from different angles, noise, etc.
  • Triangulation using distance between each camera, focal points, and relative positions of the corresponding segments
  • Overview of Human and Computer Vision

    1. 1. "The eye doesn't see any shapes, it sees only what is differentiated through light and dark or through colors." -Johann Wolfgang Von Goethe (1749–1832), German poet.
    2. 2. An Overview of Human and Computer Vision BarCamp Omaha 2010 Corey A. Spitzer
    3. 3. Hi.
    4. 4. The Eye http://en.wikipedia.org/wiki/File:Diagram_of_eye_evolution.svg
    5. 5. The Retina http://www.colorado.edu/intphys/Class/IPHY3730/07vision.html
    6. 6. The Retina http://openwetware.org/wiki/Image:Ch11f12.gif
    7. 7. Beyond the Retina http://en.wikipedia.org/wiki/File:ERP_-_optic_cabling.jpg (Attribution: Ratznium at en.wikipedia )
    8. 8. Beyond the Retina http://langabi.name/blog/2005/09/26/optical-illusions-and-visual-phenomena
    9. 9. Beyond the Retina http://langabi.name/blog/2005/09/26/optical-illusions-and-visual-phenomena
    10. 10. Computer Vision http://en.wikipedia.org/wiki/File:Studijskifotoaparat.JPG
    11. 11. Low-level Image Processing http://en.wikipedia.org/wiki/File:Aliasing_a.png http://en.wikipedia.org/wiki/Histogram_equalization
    12. 12. Image Segmentation http://en.wikipedia.org/wiki/File:EdgeDetectionMathematica.png Edge Detection
    13. 13. Image Segmentation http://people.cs.uchicago.edu/~pff/segment/ Region-based Segmentation
    14. 14. Object and Facial Recognition
    15. 15. High-level Image Processing brosnan et. al.*
    16. 16. Movement / Object Tracking http://www.youtube.com/watch?v=OjLlZJTahUw&t=1m12s
    17. 17. Depth Perception using Structured Light http://www.youtube.com/watch?v=rYD6L1X1GUI http://www.youtube.com/watch?v=854ZTvs8UoU&t=6m42s
    18. 18. Stereopsis
    19. 19. Stereopsis Ogale and Aloimonos**
    20. 20. Stereopsis
    21. 21. Stereopsis
    22. 22. Stereopsis ~ 37.13 cm
    23. 23. Stereopsis ~ 23.52 cm
    24. 24. Sources and Further Information Brain and Behavior course website University of Colorado at Boulder http://www.colorado.edu/intphys/Class/IPHY3730/07vision.html * Improving quality inspection of food products by computer vision––a review Tadhg Brosnan, Da-Wen Sun ** Shape and the stereo correspondence problem Abhijit S. Ogale and Yiannis Aloimonos
    25. 25. Sources and Further Information

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