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- 1. EDGE DETECTION Presentation by Sarbjeet Singh(National Institute of Technical Teachers Training and research) Chandigarh
- 2. CONTENTS Introduction Types of Edges Steps in Edge Detection Methods of Edge Detection First Order Derivative Methods First Order Derivative Methods - Summary Second Order Derivative Methods Second Order Derivative Methods - Summary Optimal Edge Detectors Canny Edge Detection Edge Detector Performance Application areas05/15/12 ECE - Sarbjeet Singh 2
- 3. INTRODUCTION Edge - Area of significant change in the image intensity / contrast Edge Detection – Locating areas with strong intensity contrasts Use of Edge Detection – Extracting information about the image. E.g. location of objects present in the image, their shape, size, image sharpening and enhancement05/15/12 ECE - Sarbjeet Singh 3
- 4. TYPES OF EDGES Variation of Intensity / Gray Level Step Edge Ramp Edge Line Edge Roof Edge05/15/12 ECE - Sarbjeet Singh 4
- 5. Steps in Edge Detection Filtering – Filter image to improve performance of the Edge Detector wrt noise Enhancement – Emphasize pixels having significant change in local intensity Detection – Identify edges - thresholding Localization – Locate the edge accurately, estimate edge orientation05/15/12 ECE - Sarbjeet Singh 5
- 6. Noisy Image Example of Noisy Image05/15/12 ECE - Sarbjeet Singh 6
- 7. METHODS OF EDGE DETECTION First Order Derivative / Gradient Methods Roberts Operator Sobel Operator Prewitt Operator Second Order Derivative Laplacian Laplacian of Gaussian Difference of Gaussian Optimal Edge Detection Canny Edge Detection05/15/12 ECE - Sarbjeet Singh 7
- 8. First Derivative • At the point of greatest slope, the first derivative has maximum value – E.g. For a Continuous 1- dimensional function f(t)05/15/12 ECE - Sarbjeet Singh 8
- 9. Gradient For a continuous two dimensional function Gradient is defined as ∂f Gx ∂x G[ f ( x, y )] = = Gy ∂f ∂y G = Gx 2 + Gy 2 ≈ Gx + Gy Gy θ = tan −1 Gx 05/15/12 ECE - Sarbjeet Singh 9
- 10. Gradient Approximation of Gradient for a Gx ≅ f [i, j + 1] − f [i, j ] discrete two dimensional function Convolution Mask Gy ≅ f [i, j ] − f [i + 1, j ] Gx= -1 1 -1 1 Gy = 1 -1 1 -1 1 1 Differences are computed at the -1 -1 interpolated points [i, j+1/2] and [i+1/2, j]05/15/12 ECE - Sarbjeet Singh 10
- 11. Gradient Methods – Roberts Operator Provides an approximation to the gradient G[ f (i, j )] = Gx + Gy = f (i, j ) − f (i + 1, j + 1) + f (i + 1, j ) − f (i, j + 1) Convolution Mask Gx= 1 0 Gy = 0 -1 0 -1 1 0 Differences are computed at the interpolated points [i+1/2, j+1/2] and not [i, j]05/15/12 ECE - Sarbjeet Singh 11
- 12. Roberts Operator - Example The output image has been scaled by a factor of 5 Spurious dots indicate that the operator is susceptible to noise05/15/12 ECE - Sarbjeet Singh 12
- 13. Gradient Methods – Sobel Operator The 3X3 convolution mask smoothes the image by some amount , hence it is less susceptible to noise. But it produces thicker edges. So edge localization is poor Convolution Mask -1 0 1 1 2 1 Gx = -2 0 2 Gy= 0 0 0 -1 0 1 -1 -2 -1 The differences are calculated at the center pixel of the mask.05/15/12 ECE - Sarbjeet Singh 13
- 14. Sobel Operator - Example Compare the output of the Sobel Operator with that of the Roberts Operator: The spurious edges are still present but they are relatively less intense compared to genuine lines Roberts operator has missed a few edges Sobel operator detects thicker edges Will become more clear with the final demo Outputs of Sobel (top) and Roberts operator05/15/12 ECE - Sarbjeet Singh 14
- 15. Gradient Methods – PrewittOperator It is similar to the Sobel operator but uses slightly different masks Convolution Mask Px = -1 0 1 -1 0 1 -1 0 1 Py = 1 1 1 0 0 0 -1 -1 -105/15/12 ECE - Sarbjeet Singh 15
- 16. First Order Derivative Methods -Summary Noise – simple edge detectors are affected by noise – filters can be used to reduce noise Edge Thickness – Edge is several pixels wide for Sobel operator– edge is not localized properly Roberts operator is very sensitive to noise Sobel operator goes for averaging and emphasizes on the pixel closer to the center of the mask. It is less affected by noise and is one of the most popular Edge Detectors.05/15/12 ECE - Sarbjeet Singh 16
- 17. Second Order Derivative Methods Zero crossing of the second derivative of a function indicates the presence of a maxima 05/15/12 ECE - Sarbjeet Singh 17
- 18. Second Order Derivative Methods -Laplacian Defined as Mask 0 1 0 1 -4 1 0 1 0 Very susceptible to noise, filtering required, use Laplacian of Gaussian 05/15/12 ECE - Sarbjeet Singh 18
- 19. Second Order Derivative Methods - Laplacian of Gaussian Also called Marr-Hildreth Edge Detector Steps Smooth the image using Gaussian filter Enhance the edges using Laplacian operator Zero crossings denote the edge location Use linear interpolation to determine the sub-pixel location of the edge05/15/12 ECE - Sarbjeet Singh 19
- 20. Laplacian of Gaussian – contd. Defined as Greater the value of σ, broader is the Gaussian filter, more is the smoothing Too much smoothing may make the detection of edges difficult05/15/12 ECE - Sarbjeet Singh 20
- 21. Laplacian of Gaussian - contd. Also called the Mexican Hat operator05/15/12 ECE - Sarbjeet Singh 21
- 22. Laplacian of Gaussian – contd. Mask Discrete approximation to LoG function with Gaussian = 1.405/15/12 ECE - Sarbjeet Singh 22
- 23. Second Order Derivative Methods - Difference of Gaussian - DoG LoG requires large computation time for a large edge detector mask To reduce computational requirements, approximate the LoG by the difference of two LoG – the DoG x 2+ y 2 x 2+ y 2 −( ) −( ) 2πσ12 2πσ 2 2 e e DoG ( x, y ) = − 2πσ 1 2πσ 2 2 205/15/12 ECE - Sarbjeet Singh 23
- 24. Difference of Gaussian – contd. Advantage of DoG Close approximation of LoG Less computation effort Width of edge can be adjusted by changing σ1 and σ205/15/12 ECE - Sarbjeet Singh 24
- 25. Second Order Derivative Methods - Summary Second Order Derivative methods especially Laplacian, are very sensitive to noise Probability of false and missing edges remain Localization is better than Gradient Operators05/15/12 ECE - Sarbjeet Singh 25
- 26. Optimal Edge Detector Optimal edge detector depending on Low error rate – edges should not be missed and there must not be spurious responses Localization – distance between points marked by the detector and the actual center of the edge should be minimum Response – Only one response to a single edge One dimensional formulation Assume that 2D images have constant cross section in some direction05/15/12 ECE - Sarbjeet Singh 26
- 27. First Criterion: Edge Detection Response of filter to the edge: RMS response of filter to noise : First criterion: output Signal to Noise Ratio05/15/12 ECE - Sarbjeet Singh 27
- 28. Second Criterion: Edge Localization A measure that increases as the localization increases is needed Reciprocal of RMS distance of the marked edge from center of true edge is taken as the measure of localization Localization is defined as:05/15/12 ECE - Sarbjeet Singh 28
- 29. Third Criterion – Elimination ofmultiple responses In presence of noise several maxima are detected – it is difficult to separate noise from edge We try to obtain an expression for the distance between adjacent noise peaks The mean distance between the adjacent maxima in the output is twice the distance between the adjacent zero crossings in the derivative of output operator05/15/12 ECE - Sarbjeet Singh 29
- 30. Noise estimation Important to estimate the amount of noise in the image to set thresholds Noise component can be efficiently isolated using Weiner Filtering – requires the knowledge of the autocorrelation of individual components and their cross- correlation Noise strength is estimated by Global Histogram Estimation05/15/12 ECE - Sarbjeet Singh 30
- 31. Thresholding Broken edges due to fluctuation of operator output above and below the threshold – results in Streaking Use double thresholding to eliminate streaking05/15/12 ECE - Sarbjeet Singh 31
- 32. Two Dimensional Edge Detection In two dimensions edge has both position and direction A 2-D mask is created by convolving a linear edge detection function aligned normal to the edge direction with a projection function parallel the edge direction Projection function is Gaussian with same deviation as the detection function The image is convolved with a symmetric 2-D Gaussian and then differentiated normal to the edge direction05/15/12 ECE - Sarbjeet Singh 32
- 33. Implementation of Canny EdgeDetector Step 1 Noise is filtered out – usually a Gaussian filter is used Width is chosen carefully Step 2 Edge strength is found out by taking the gradient of the image A Roberts mask or a Sobel mask can be used G = Gx + Gy ≈ Gx + Gy 2 205/15/12 ECE - Sarbjeet Singh 33
- 34. Implementation of Canny EdgeDetector – contd. Step 3 Find the edge direction Gy θ = tan −1 Gx Step 4 Resolve edge direction05/15/12 ECE - Sarbjeet Singh 34
- 35. Canny Edge Detector – contd. Step 5 Non-maxima suppression – trace along the edge direction and suppress any pixel value not considered to be an edge. Gives a thin line for edge Step 6 Use double / hysterisis thresholding to eliminate streaking05/15/12 ECE - Sarbjeet Singh 35
- 36. Canny Edge Detector – contd. Compare the results of Sobel and Canny05/15/12 ECE - Sarbjeet Singh 36
- 37. Edge Detector Performance Criteria Probability of false edges Probability of missing edges Error in estimation of edge angle Mean square distance of edge estimate from true edge Tolerance to distorted edges and other features such as corners and junctions05/15/12 ECE - Sarbjeet Singh 37
- 38. Figure of Merit Basic errors in a Edge Detector Missing Edges Error in localizing Classification of noise as Edge IA 1 1 FM = ∑1+ d α 2 max( I A , I I ) i =1 i IA : detected edges II : ideal edges d : distance between actual and ideal edges α : penalty factor for displaced edges05/15/12 ECE - Sarbjeet Singh 38
- 39. Applications Enhancement of noisy images – satellite images, x-rays, medical images like cat scans Text detection Mapping of roads Video surveillance, etc.05/15/12 ECE - Sarbjeet Singh 39
- 40. Applications Canny Edge Detector for Remote Sensing Images Reasons to go for Canny Edge Detector – Remote sensed images are inherently noisy Other edge detectors are very sensitive to noise05/15/12 ECE - Sarbjeet Singh 40
- 41. Edge map of remote sensed image using Canny 05/15/12 ECE - Sarbjeet Singh 41
- 42. 05/15/12 ECE - Sarbjeet Singh 42
- 43. Thank You05/15/12 ECE - Sarbjeet Singh 43
- 44. References Machine Vision – Ramesh Jain, Rangachar Kasturi, Brian G Schunck, McGraw-Hill, 1995 INTRODUCTION TO COMPUTER VISION AND IMAGE PROCESSING - by Luong Chi Mai Department of Pattern Recognition and Knowledge Engineering I Institute of Information Technology, Hanoi, Vietnam http://www.netnam.vn/unescocourse/computervision/computer.htm The Hypermedia Image Processing Reference - http://homepages.inf.ed.ac.uk/rbf/HIPR2/hipr_top.htm A Survey and Evaluation of Edge Detection Operators Application to Medical Images – Hanene Trichili, Mohamed-Salim Bouhlel, Nabil Derbel, Lotfi Kamoun, IEEE, 2002 Using The Canny Edge Detector for Feature Extraction and Enhancement of Remote Sensing Images - Mohamed Ali David Clausi, Systems Design Engineering, University of Waterloo, IEEE 2001 A Computational Approach to Edge Detection – John Canny, IEEE, 198605/15/12 ECE - Sarbjeet Singh 44

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