1. Computer Vision Group Edge Detection Giacomo Boracchi 5/12/2007 boracchi@elet.polimi.it

2. Computer Vision Group Edge Detection in Images  Goal: Automatically find the contour of objects in a scene.  What For: Edges are significant descriptors, useful for classification, compression…

3. Computer Vision Group Edge Detection in Images  What is an object? It is one of the goals of computer vision to identify objects in scenes.

4. Computer Vision Group Edges May Have Different Sources  Object Boundaries  Occlusions  Textures  Shading

5. Computer Vision Group What is an Edge  Lets define an edge to be a discontinuity in image intensity function.  Several Models Step Edge Ramp Edge Roof Edge Spike Edge  They can be thus detected as discontinuities of image Derivatives

6. Computer Vision Group Differentiation and convolution  Recall  Now this is linear and shift invariant. Therefore, in discrete domain, it will be represented as a convolution

7. Computer Vision Group Differentiation and convolution  Recall  Now this is linear and shift invariant, so must be the result of a convolution.  We could approximate this as (which is obviously a convolution with Kernel ; it’s not a very good way to do things, as we shall see)

8. Computer Vision Group Finite Difference in 2D Discrete Approximation Horizontal Convolution Kernels Vertical

9. Computer Vision Group Finite differences

10. Computer Vision Group Noise  Simplest noise model independent stationary additive Gaussian noise the noise value at each pixel is given by an independent draw from the same normal probability distribution  Issues this model allows noise values that could be greater than maximum camera output or less than zero for small standard deviations, this isn’t too much of a problem - it’s a fairly good model independence may not be justified (e.g. damage to lens) may not be stationary (e.g. thermal gradients in the ccd)

11. Computer Vision Group Finite differences and noise  Finite difference filters respond strongly to noise obvious reason: image noise results in pixels that look very different from their neighbors  The more relevant is the noise, the stronger the response

12. Computer Vision Group Finite differences and noise  What is to be done? intuitively, most pixels in images look quite a lot like their neighbors. this is true even at an edge; along the edge they’re similar, across the edge they’re not. suggests that smoothing the image should help, by forcing pixels different to their neighbors (=noise pixels?) to look more like neighbors.

13. Computer Vision Group Finite differences responding to noise Increasing noise -> (this is zero mean additive gaussian noise)

14. Computer Vision Group Finite differences responding to noise Increasing noise -> (this is zero mean additive gaussian noise)

15. Computer Vision Group Finite differences responding to noise Increasing noise -> (this is zero mean additive gaussian noise)

16. Computer Vision Group Smoothing reduces noise  Generally expect pixels to “be like” their neighbors surfaces turn slowly relatively few reflectance changes  Generally expect noise processes to be independent from pixel to pixel

17. Computer Vision Group Smoothing reduces noise  Generally expect pixels to “be like” their neighbors surfaces turn slowly relatively few reflectance changes  Generally expect noise processes to be independent from pixel to pixe  Implies that smoothing suppresses noise, (i.i.d. noise!)  Gaussian Filtering the parameter in the symmetric Gaussian as this parameter goes up, more pixels are involved in the average and the image gets more blurred and noise is somehow suppressed

18. Computer Vision Group The effects of smoothing Each row shows smoothing with gaussians of different width; each column shows different realisations of an image of gaussian noise. Low - Pass

19. Computer Vision Group Classical Operators : Prewitt Smooth Differentiate Horizontal Vertical

20. Computer Vision Group Classical Operators: Sobel Smooth Differentiate Horizontal Vertical

21. Computer Vision Group Gaussian Filter

22. Computer Vision Group  Sobel Edge Detector Detecting Edges in Image Image I Threshold Edges any alternative ? Discrete Derivatives Gradient Norms Threshold

23. Computer Vision Group Quality of an Edge Detector  Robustness to Noise  Localization  Too Many/Too less Responses Poor robustness to noise Poor localization Too many responses True Edge

24. Computer Vision Group Canny Edge Detector Criteria  Good Detection: The optimal detector must minimize the probability of false positives as well as false negatives.  Good Localization: The edges detected must be as close as possible to the true edges.  Single Response Constraint: The detector must return one point only for each edge point.

25. Computer Vision Group Canny Edge Detector  Basically Convolution with derivative of Gaussian Non-maximum Suppression Hysteresis Thresholding

26. Computer Vision Group Canny Edge Detector  Smooth by Gaussian  Compute x and y derivatives  Compute gradient magnitude and orientation

27. Computer Vision Group Canny Edge Operator

28. Computer Vision Group Canny Edge Detector

29. Computer Vision Group The Gradient Orientation  Like for continuous function, the gradient at each pixel points at the steepest intensity growth direction.  The gradient norm indicates the inclination of the intensity growth.  Matlab…..

30. Computer Vision Group Canny Edge Detector

31. Computer Vision Group We wish to mark points along the curve where the magnitude is biggest. We can do this by looking for a maximum along a slice normal to the curve (non-maximum suppression). These points should form a curve. There are then two algorithmic issues: at which point is the maximum, and where is the next one? Non-Maximum Suppression - Idea

32. Computer Vision Group We wish to mark points along the curve where the magnitude is biggest. We can do this by looking for a maximum along a slice normal to the curve (non-maximum suppression). These points should form a curve. There are then two algorithmic issues: at which point is the maximum, and where is the next one? Non-Maximum Suppression – Idea (II)

33. Computer Vision Group Non-Maximum Suppression - Threshold  Suppress the pixels in ‘Gradient Magnitude Image’ which are not local maximum

34. Computer Vision Group An Overview on Threshold  According to the way the Threshold T is used they are divided into Global Threshold Local (or) Adaptive Threshold  According to the output they can be classified in Binary Threshold Hard Threshold Soft Threshold

35. Computer Vision Group Non-Maximum Suppression

36. Computer Vision Group Hysteresis Thresholding  Use of two different threshold High and Low for For new edge starting point For continuing edges  In such a way the edges continuity is preserved

37. Computer Vision Group Hysteresis Thresholding  If the gradient at a pixel is above ‘High’, declare it an ‘edge pixel’.  If the gradient at a pixel is below ‘Low’, declare it a ‘non- edge-pixel’.  If the gradient at a pixel is between ‘Low’ and ‘High’ then declare it an ‘edge pixel’ if and only if it is connected to an ‘edge pixel’ directly or via pixels between ‘Low’ and ‘ High’.

38. Computer Vision Group Hysteresis Thresholding

39. Computer Vision Group Examples an image its detected edges

40. Computer Vision Group In matlab..  Canny edge detector is implemented with the edge.m command

41. Computer Vision Group A brief overview on Morphological Operators in Image Processing Giacomo Boracchi 5/12/2007 boracchi@elet.polimi.it

42. Computer Vision Group An overview on morphological operations  Erosion, Dilation  Open, Closure  We assume the image being processed is binary, as these operators are typically meant for refining “mask” images.

43. Computer Vision Group AND operator in Binary images Images From Digital Image Processing II edition Gonzalez & Woods Chapter 9

44. Computer Vision Group OR in Binary Images

45. Computer Vision Group Erosion  General definition: Nonlinear Filtering procedure that replace to each pixel value the maximum on a given neighbor  As a consequence on binary images E(x)=1 iff the image in the neighbor is constantly 1  This operation reduces thus the boundaries of binary images  It can be interpreted as an AND operation of the image and the neighbor overlapped at each pixel

46. Computer Vision Group Erosion Example

47. Computer Vision Group Erosion Example

48. Computer Vision Group Dilation  General definition: Nonlinear Filtering procedure that replace to each pixel value the minimum on a given neighbor  As a consequence on binary images E(x)=1 iff at least a pixel in the neighbor is1  This operation grows fat the boundaries of binary images  It can be interpreted as an OR operation of the image and the neighbor overlapped at each pixel

49. Computer Vision Group Dilation Example

50. Computer Vision Group Dilation Example

51. Computer Vision Group In matlab  They are performed using the bwmorph.m script passing the name of the operation as a parmeter Examples…

52. Computer Vision Group Open and Closure  Open Erosion followed by a Dilation  Closure Dilation followed by an Erosion

53. Computer Vision Group Open  Open Erosion followed by a Dilation Smoothes the contours of an object Typically eliminate thin protrusions

54. Computer Vision Group Open  Figure to Open,  Structuring element a Disk

55. Computer Vision Group Open  First Erode,

56. Computer Vision Group Open  Then Dilate,  Open results, the bridge has been eliminate from the first erosion and cannot be replaced by the dilation.  Edges are smoothed  Corners are rounded

57. Computer Vision Group Closure  Closure Dilation followed by an Erosion Smoothes the contours of an object, typically creates Generally fuses narrow breaks

58. Computer Vision Group Open  Figure to Open,  Structuring element a Disk

59. Computer Vision Group Close  First Dilate,

60. Computer Vision Group Close  Then Erode,  Close results, the bridge has been preserved  Edges are smoothed  Corners are rounded