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Region description

Region description. Information that let’s you recognise a region. Introduction. Region detection isolates regions that differ from neighbours Description identifies property values Labelling identifies regions. Contents. Features derived from binary images Structure Region (CCA) Shape

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Region description

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  1. Region description Information that let’s you recognise a region.

  2. Introduction • Region detection isolates regions that differ from neighbours • Description identifies property values • Labelling identifies regions Image Processing and Computer Vision: 5

  3. Contents • Features derived from binary images • Structure • Region (CCA) • Shape • Texture • Surface shape Image Processing and Computer Vision: 5

  4. Features derived from binary images • Connected component analysis • Perimeter • Area Image Processing and Computer Vision: 5

  5. Connected Component Analysis • To identify groups of connected pixels Image Processing and Computer Vision: 5

  6. 1 2 3 4 ? Algorithm First pass If a neighbour is labelled Or two neighbours have same label Pixel receives same label; If two neighbours have different labels Pixel receives one label, equivalence is recorded Second pass Relabel all equivalent labels Image Processing and Computer Vision: 5

  7. Borders • Straight lines • Chain codes • Polylines • Curved lines • Splines • Circles • Phi-S • Snakes Image Processing and Computer Vision: 5

  8. 0 1 7 6 2 3 5 4 Chain Codes Trace the object outline - follow pixels on boundary Code directions of movement Description is position independent, orientation dependent Can use differential chain codes Image Processing and Computer Vision: 5

  9. Perimeter From Chain Code Even codes have length 1 Odd codes have length 2 Perimeter length = #even + 2 #odd Image Processing and Computer Vision: 5

  10. 7 0 1 2 3 4 5 6 h 0 h+1/2 h 0 -h+1/2 -h -h-1/2 h-1/2 Area From Chain Code h is measured from an arbitrary datum, e.g. y co-ordinate of start of codes. Image Processing and Computer Vision: 5

  11. Crack Codes • These follow pixel boundaries • Not pixel centres • Same representation of displacement • Longer coding • More accurate Image Processing and Computer Vision: 5

  12. Image Processing and Computer Vision: 5

  13. Polyline Representation • Represent the line by a set of joined line segments • Polyline and original endpoints coincide • Segments interpolate edge points • Computed by curve splitting or segment merging • Decomposing initial curve • Combining curve segments Image Processing and Computer Vision: 5

  14. Polyline Splitting For each curve segment D = maximum distance of segment to line between endpoints If D > threshold Insert a vertex Image Processing and Computer Vision: 5

  15. Segment Merging • May be necessary between endpoints of adjacent segments • Use edge following techniques Image Processing and Computer Vision: 5

  16. Polyline representation is suitable for linear sections Curved sections are inefficiently represented Alternatives Splines Circles Curved Line Sections Image Processing and Computer Vision: 5

  17. B-Splines • A curve represented by control points • Endpoints fixed by two control points • Shape controlled by two control points Image Processing and Computer Vision: 5

  18. If control points can be found • Curve is compactly represented Image Processing and Computer Vision: 5

  19. Fourier Descriptors • Represent co-ordinates of boundary points as complex numbers • They can be Fourier transformed • Coefficients of transform are the Fourier descriptors • Retain more or fewer according to desired accuracy Image Processing and Computer Vision: 5

  20. Example Image Processing and Computer Vision: 5

  21. s  Phi-S Curves • (i, si) • characteristic of the object’s shape • independent of location • dependent on orientation Image Processing and Computer Vision: 5

  22. Snakes, Active/Dynamic Contours • Borders follow outline of object • Outline obscured? • Snake provides a solution Image Processing and Computer Vision: 5

  23. Algorithm • Snake computes smooth, continuous border • Minimises • Length of border • Curvature of border • Against an image property • Gradient? Image Processing and Computer Vision: 5

  24. Minimisation • Initialise snake • Integrate energy along it • Iteratively move snake to global energy minimum Image Processing and Computer Vision: 5

  25. Image Processing and Computer Vision: 5

  26. Texture • Two definitions • A pseudoregular arrangement of a primitive element • A pseudorandom distribution of brightness values Image Processing and Computer Vision: 5

  27. Examples Image Processing and Computer Vision: 5

  28. Classification • A useful property for identifying surfaces • Aerial photographs • Medical imagery Image Processing and Computer Vision: 5

  29. Structural Texture Representations • Require • Texture primitive - texel • Placement rule • Ideal for regular - man-made - textures Image Processing and Computer Vision: 5

  30. Fourier Descriptors • Placement rule  periodicity • Can use • Autocorrelation • Fourier transform • To recognise it Image Processing and Computer Vision: 5

  31. Fourier Descriptor • Compute modulus of transform • Energy in different regions is characteristic of texture Image Processing and Computer Vision: 5

  32. Markov Random Field Representations • Each pixel value a combination of neighbours plus noise • Find coefficients of model • Characterise texture • Least squares minimisation Image Processing and Computer Vision: 5

  33. Statistical Descriptions • Better suited to pseudorandom, natural textures • First Order statistics • Second order statistics Image Processing and Computer Vision: 5

  34. First Order Statistics • Statistical descriptions of grey level distribution • Mean grey value • Deviation of grey values • Coefficient of variation • etc. • Can give useful results • Generally too sensitive to factors other than identity of surface Image Processing and Computer Vision: 5

  35. Second Order Statistics • Measures involving multiple pixels • Joint difference histogram • Histogram of differences between adjacent pixels • Co-Occurrence matrices • Measure frequency of specific pairs of grey values Image Processing and Computer Vision: 5

  36. Co-Occurrence Matrices • Define a relative separation vector • e.g. 3 pixels across, 2 up • Use each pair of pixels separated by the vector as matrix indices • Increment this matrix element • Shape of matrix characterises the texture • Can be characterised by factors derived from it. Image Processing and Computer Vision: 5

  37. Edge Frequency • Density of microedges is characteristic of texture • Apply an edge detector • Sobel is suitable • Threshold result • Compute density of edge elements Image Processing and Computer Vision: 5

  38. Shape from … • To recover shapes of objects in a scene • By identifying spatial properties of surface patches Image Processing and Computer Vision: 5

  39. Shape from Motion • From • 4 views • Of • 3 non-colinear points • Can compute • motion and relative locations of points Image Processing and Computer Vision: 5

  40. Shape from Photometric Stereo • Capture images of a scene in two cameras • Must know • Cameras’ separation • Cameras’ relative orientation (parallel in example) • Co-ordinates of corresponding points in images Image Processing and Computer Vision: 5

  41. Image plane Optical centres Scene (x, y,z) x camera 1 (x’, y’, f) d x+d centre line d camera 2 z f (x’’, y’’, f) Plan view of cameras’ optical paths. Image Processing and Computer Vision: 5

  42. Image Processing and Computer Vision: 5

  43. Provided that cameras are aligned separation is known corresponding points are identified The point’s depth can be computed. Correspondence problem examined later. Image Processing and Computer Vision: 5

  44. Shape from Shading • For matt surfaces, proportion of incident light reflected depends on • Surface reflectance • Surface orientation with respect to light source Image Processing and Computer Vision: 5

  45. If k can be estimated • Image value for q = 0 • Can estimate cos q, hence q throughout image. • Surface orientation is not determined uniquely • Two angles are needed Image Processing and Computer Vision: 5

  46. Shape from Texture • Apparent texture of a surface is dependent on the surface’s • Orientation • Range Image Processing and Computer Vision: 5

  47. Method • Must be able to identify fundamental texture elements • Assume they are invariant • Compute mapping to transform each element to a standard appearance • Mapping determines surface orientation. Image Processing and Computer Vision: 5

  48. Image Processing and Computer Vision: 5

  49. Summary • Binary image features • Skeleton • Boundaries • Texture • Shape from … Image Processing and Computer Vision: 5

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