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Image processing9 segmentation(pointslinesedges)
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  • 1. Course Website: http://www.comp.dit.ie/bmacnameeDigital Image ProcessingImage Segmentation:Thresholding
  • 2. 2of20ContentsSo far we have been considering imageprocessing techniques used to transformimages for human interpretationToday we will begin looking at automatedimage analysis by examining the thorny issueof image segmentation:– The segmentation problem– Finding points, lines and edges
  • 3. 3of20The Segmentation ProblemSegmentation attempts to partition the pixelsof an image into groups that stronglycorrelate with the objects in an imageTypically the first step in any automatedcomputer vision application
  • 4. 4of20Segmentation ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 5. 5of20Detection Of DiscontinuitiesThere are three basic types of grey leveldiscontinuities that we tend to look for indigital images:– Points– Lines– EdgesWe typically find discontinuities using masksand correlation
  • 6. 6of20Point DetectionPoint detection can be achieved simplyusing the mask below:Points are detected at those pixels in thesubsequent filtered image that are above aset thresholdImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 7. 7of20Point Detection (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)X-ray image ofa turbine bladeResult of pointdetectionResult ofthresholding
  • 8. 8of20Line DetectionThe next level of complexity is to try todetect linesThe masks below will extract lines that areone pixel thick and running in a particulardirectionImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 9. 9of20Line Detection (cont…)ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)Binary image of a wirebond maskAfterprocessingwith -45° linedetectorResult ofthresholdingfiltering result
  • 10. 10of20Edge DetectionAn edge is a set of connected pixels that lieon the boundary between two regionsImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 11. 11of20Edges & DerivativesWe have already spokenabout how derivativesare used to finddiscontinuities1stderivative tells uswhere an edge is2ndderivative canbe used to showedge directionImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 12. 12of20Derivatives & NoiseDerivative based edge detectors areextremely sensitive to noiseWe need to keep this in mindImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 13. 13of20Common Edge DetectorsGiven a 3*3 region of an image the followingedge detection filters can be usedImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 14. 14of20Edge Detection ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)Original Image Horizontal Gradient ComponentVertical Gradient Component Combined Edge Image
  • 15. 15of20Edge Detection ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 16. 16of20Edge Detection ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 17. 17of20Edge Detection ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 18. 18of20Edge Detection ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 19. 19of20Edge Detection ProblemsOften, problems arise in edge detection inthat there are is too much detailFor example, the brickwork in the previousexampleOne way to overcome this is to smoothimages prior to edge detection
  • 20. 20of20Edge Detection Example WithSmoothingImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)Original Image Horizontal Gradient ComponentVertical Gradient Component Combined Edge Image
  • 21. 21of20Laplacian Edge DetectionWe encountered the 2nd-order derivativebased Laplacian filter alreadyThe Laplacian is typically not used by itselfas it is too sensitive to noiseUsually hen used for edge detection theLaplacian is combined with a smoothingGaussian filterImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 22. 22of20Laplacian Of GaussianThe Laplacian of Gaussian (or Mexican hat)filter uses the Gaussian for noise removaland the Laplacian for edge detectionImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 23. 23of20Laplacian Of Gaussian ExampleImagestakenfromGonzalez&Woods,DigitalImageProcessing(2002)
  • 24. 24of20SummaryIn this lecture we have begun looking atsegmentation, and in particular edge detectionEdge detection is massively important as it isin many cases the first step to objectrecognition