1. 2015-10-27 EIE426-AICV 1 Computer Vision Filename: eie426-computer-vision-0809.ppt

2. 2015-10-27 EIE426-AICV 2 Contents Perception generally Image formation Color vision Edge detection Image segmentation Visual attention 2D  3D Object recognition

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! 2015-10-27 EIE426-AICV 3 Missing depth information!

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 2015-10-27 EIE426-AICV 4

5. Vision “subsystems” 2015-10-27 EIE426-AICV 5 Vision requires combining multiple cues

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! 2015-10-27 EIE426-AICV 6

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

8. Images 2015-10-27 EIE426-AICV 8

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 2015-10-27 EIE426-AICV 9

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 2015-10-27 EIE426-AICV 10

11. Color vision (cont.) 2015-10-27 EIE426-AICV 11

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. 2015-10-27 EIE426-AICV 12

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.) 2015-10-27 EIE426-AICV 13

14. Edge detection (cont.) 2015-10-27 EIE426-AICV 14

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

16. Edge detection (cont.) 2015-10-27 EIE426-AICV 16 A color picture of a steam engine. The Sobel operator applied to that image.

17. Edge detection: application 1 2015-10-27 EIE426-AICV 17 An edge extraction based method to produce the pen- and-ink like drawings from photos

18. 2015-10-27 EIE557-CI&IA 18 Leaf (vein pattern) characterization Edge detection: application 2

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. 2015-10-27 EIE426-AICV 19

20. Image segmentation (cont.) 2015-10-27 EIE426-AICV 20

21. Image segmentation: the quadtree partition based split-and-merge algorithm (1) Split into four disjoined quadrants any region R i where P(R i ) = FALSE. (2) Merge any adjacent regions R i and R k for which P(R i  R k ) = TRUE; and (3) Stop when no further merging or splitting is possible. P(R i ) = TRUE if all pixels in R i have the same intensity or are uniform in some measure. 2015-10-27 EIE426-AICV 21

22. Image segmentation: the quadtree partition based split-and-merge algorithm (cont.) 2015-10-27 EIE426-AICV 22

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. 2015-10-27 EIE426-AICV 23

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. 2015-10-27 EIE426-AICV 24

25. Attention-driven object extraction 2015-10-27 EIE426-AICV 25 The more attentive a object/region, the higher priority it has

26. Attention-driven object extraction (cont.) 2015-10-27 EIE426-AICV 26 Objects 1, 2, …,background

27. Motion 2015-10-27 EIE426-AICV 27 The rate of apparent motion can tell us something about distance. A nearer object has a larger motion. Object tracking

28. Motion Estimation 2015-10-27 EIE426-AICV 28

29. Stereo 2015-10-27 EIE426-AICV 29 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

30. Disparity and depth 2015-10-27 EIE426-AICV 30

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

32. Example: Electronic eyes for the blind 2015-10-27 EIE426-AICV 32

33. Example: Electronic eyes for the blind (cont.) 2015-10-27 EIE426-AICV 33 Nearer Farther object Left camera Right camera Left captured image Right captured image Pixels matching for calculating the disparities

34. Example: Electronic eyes for the blind (cont.) 2015-10-27 EIE426-AICV 34 Left: x=549 Right: x=476 ∆=73 Left: x=333 Right: x=273 ∆=60

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. 2015-10-27 EIE426-AICV 35

36. Edge and vertex types 2015-10-27 EIE426-AICV 36 “+” 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.

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 2015-10-27 EIE426-AICV 37

38. Biometric identification Criminal investigations and access control for restricted facilities require the ability to indentify unique individuals. 2015-10-27 EIE426-AICV 38 (the blueish area)

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. 2015-10-27 EIE426-AICV 39

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 http://labs.systemone.at/retrievr/?sketchName=2009-03-26-01-22-37-828150.3#sketchName=2009-03-26-01-23- 35-358087.4 2015-10-27 EIE426-AICV 40

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 2015-10-27 EIE426-AICV 41

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. 2015-10-27 EIE426-AICV 42