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Computer Vision

Computer Vision. Filename: eie426-computer-vision-0809.ppt. Contents. Perception generally Image formation Color vision Edge detection Image segmentation Visual attention 2D  3D Object recognition. Perception generally. Stimulus (percept) S, World W S = g(W)

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Computer Vision

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  1. Computer Vision Filename: eie426-computer-vision-0809.ppt EIE426-AICV

  2. Contents • Perception generally • Image formation • Color vision • Edge detection • Image segmentation • Visual attention • 2D  3D • Object recognition EIE426-AICV

  3. Perception generally Stimulus (percept) S, World W S = g(W) E.g., g = “graphics." Can we do vision as inverse graphics? W = g-1(S) Problem: massive ambiguity! Missing depth information! EIE426-AICV

  4. Better approaches Bayesian inference of world configurations: P(W|S) = P(S|W) x P(W) / P(S) = α x P(S|W) x P(W) “graphics” “prior knowledge” Better still: no need to recover exact scene! Just extract information needed for • navigation • manipulation • recognition/identification EIE426-AICV

  5. Vision “subsystems” Vision requires combining multiple cues EIE426-AICV

  6. Image formation P is a point in the scene, with coordinates (X; Y; Z) P’ is its image on the image plane, with coordinates (x; y; z) x = -fX/Z; y = -fY/Z (by similar triangles) Scale/distance is indeterminate! EIE426-AICV

  7. Len systems f : the focal length of the lens

  8. Images EIE426-AICV

  9. Images (cont.) I(x; y; t) is the intensity at (x; y) at time t CCD camera 4,000,000 pixels; human eyes 240,000,000 pixels EIE426-AICV

  10. Color vision Intensity varies with frequency  infinite-dimensional signal Human eye has three types of color-sensitive cells; each integrates the signal  3-element vector intensity EIE426-AICV

  11. Color vision (cont.) EIE426-AICV

  12. Edge detection Edges are straight lines or curves in the image plane across which there is “significant” changes in image brightness. The goal of edge detection is to abstract away from messy, multi-megabyte image and towards a more compact, abstract representation. EIE426-AICV

  13. Edge detection (cont.) Edges in image  discontinuities in scene: 1) Depth discontinuities 2) surface orientation 3) reflectance (surface markings) discontinuities 4) illumination discontinuities (shadows, etc.) EIE426-AICV

  14. Edge detection (cont.) EIE426-AICV

  15. Edge detection (cont.) • Sobel operator the location of the origin (the image pixel to be processed) Other operators: Roberts (2x2), Prewitt (3x3), Isotropic (3x3)

  16. Edge detection (cont.) A color picture of a steam engine. The Sobel operator applied to that image. 2014/9/4 16 EIE426-AICV

  17. Edge detection: application 1 An edge extraction based method to produce the pen-and-ink like drawings from photos 2014/9/4 17 EIE426-AICV

  18. Edge detection: application 2 Leaf (vein pattern) characterization EIE557-CI&IA

  19. Image segmentation • In computer vision, segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels). • The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. • Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. EIE426-AICV

  20. Image segmentation (cont.) EIE426-AICV

  21. Image segmentation: the quadtree partition based split-and-merge algorithm • Split into four disjoined quadrants any region Riwhere P(Ri) = FALSE. • Merge any adjacent regions Ri and Rk for which P(Ri Rk ) = TRUE; and • Stop when no further merging or splitting is possible. P(Ri) = TRUE if all pixels in Rihave the same intensity or are uniform in some measure. EIE426-AICV

  22. Image segmentation: the quadtree partition based split-and-merge algorithm (cont.) EIE426-AICV

  23. Visual attention • Attention is the cognitive process of selectively concentrating on one aspect of the environment while ignoring other things. • Attention mechanism of human vision system has been applied to serve machine visual system for sampling data nonuniformly and utilizing its computational resources efficiently. EIE426-AICV

  24. Visual attention (cont.) • The visual attention mechanism may have at least the following basic components: (1) the selection of a region of interest in the visual field; (2) the selection of feature dimensions and values of interest; (3) the control of information flow through the network of neurons that constitutes the visual system; and (4) the shifting from one selected region to the next in time. EIE426-AICV

  25. Attention-driven object extraction The more attentive a object/region, the higher priority it has EIE426-AICV

  26. Attention-driven object extraction (cont.) Objects 1, 2, …,background EIE426-AICV

  27. Motion • The rate of apparent motion can tell us something about distance. A nearer object has a larger motion. • Object tracking EIE426-AICV

  28. Motion Estimation 2014/9/4 28 EIE426-AICV

  29. Stereo The nearest point of the pyramid is shifted to the left in the right image and to the right in the left image. Disparity (x difference in two images)  Depth EIE426-AICV

  30. Disparity and depth EIE426-AICV

  31. Disparity and depth (cont.) Depth is inversely proportional to disparity.

  32. Example: Electronic eyes for the blind EIE426-AICV

  33. Farther object Nearer Left camera Right camera Left captured image Right captured image Pixels matching for calculating the disparities Example: Electronic eyes for the blind (cont.) EIE426-AICV

  34. Example: Electronic eyes for the blind (cont.) Left: x=549 Right: x=476 ∆=73 Left: x=333 Right: x=273 ∆=60 EIE426-AICV

  35. Texture Texture: a spatially repeating pattern on a surface that can be sensed visually. Examples: the pattern windows on a building, the stitches on a sweater, The spots on a leopard’s skin, grass on a lawn, etc. EIE426-AICV

  36. Edge and vertex types • “+” and “-” labels represent convex and concave edges, respectively. These are associated with surface normal discontinuities wherein both surfaces that meet along the edge are visible. • A “” or a “” represents an occluding convex edge. As one moves in the direction of the arrow, the (visible) surfaces are to the right. • A “” or a “” represents a limb. Here, the surface curves smoothly around to occlude itself. As one moves in the direction of the twin arrow, the (visible) surfaces lies to the right. EIE426-AICV

  37. Object recognition Simple idea: - extract 3-D shapes from image - match against “shape library” Problems: - extracting curved surfaces from image - representing shape of extracted object - representing shape and variability of library object classes - improper segmentation, occlusion - unknown illumination, shadows, markings, noise, complexity, etc. Approaches: - index into library by measuring invariant properties of objects - alignment of image feature with projected library object feature - match image against multiple stored views (aspects) of library object - machine learning methods based on image statistics EIE426-AICV

  38. Biometric identification Criminal investigations and access control for restricted facilities require the ability to indentify unique individuals. (the blueish area) EIE426-AICV

  39. Content-based image retrieval • The application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. • “Content-based” means that the search will analyze the actual contents of the image. The term ‘content’ in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Without the ability to examine image content, searches must rely on metadata such as captions or keywords, which may be laborious or expensive to produce. EIE426-AICV

  40. Content-based image retrieval (cont.) • http://labs.systemone.at/retrievr/?sketchName=2009-03-26-01-22-37-828150.3#sketchName=2009-03-26-01-23-35-358087.4 EIE426-AICV

  41. Handwritten digit recognition 3-nearest-neighbor = 2.4% error 400-300-10 unit MLP (a neural network approach) = 1.6% error LeNet: 768-192-30-10 unit MLP = 0.9% error EIE426-AICV

  42. Summary Vision is hard -- noise, ambiguity, complexity Prior knowledge is essential to constrain the problem Need to combine multiple cues: motion, contour, shading, texture, stereo “Library” object representation: shape vs. aspects Image/object matching: features, lines, regions, etc. EIE426-AICV

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