Image Processing with OpenCV

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Image Processing with OpenCV

  1. 1. Image Processing with OpenCV Debayan Banerjee Co-founder, Uberlabs
  2. 2. IntroductionWhat is Image Processing?„any form of signal processing for which the input isan image; the output of image processing may beeither an image or a set of characteristics orparameters related to the image. Most image-processing techniques involve treating the image asa two-dimensional signal and applying standardsignal-processing techniques to it“
  3. 3. Examples Smoothing
  4. 4. ExamplesErosion ↔ Dilation
  5. 5. Examples Edge detection
  6. 6. ExamplesHough line transform
  7. 7. ExamplesFace detection
  8. 8. Basic ConceptsAn image is a matrix
  9. 9. Basic ConceptsA colour image has 3 2-d matrices for R, G , B
  10. 10. Basic conceptsExample
  11. 11. Basic operations: OpenCVReading and displaying images
  12. 12. Basic operations: OpenCVWriting images
  13. 13. Core module: OpenCVAccessing individual pixels
  14. 14. Core module: OpenCVContrast and Brightness adjustment g(x) = a f(x) + b a = Contrast parameter b = Brightness parameter
  15. 15. Core module: OpenCVContrast and Brightness example a =2.2 b=50
  16. 16. Core module: OpenCVDrawing functionsLinesCirclesEllipsesPolygon
  17. 17. Image ProcessingSmoothing – Removes noiseUses filters like Gaussian, Median, BilateralmedianBlur ( src, dst, i );GaussianBlur( src, dst, Size( i, i ), 0, 0 );bilateralFilter ( src, dst, i, i*2, i/2 );
  18. 18. Image Processing Smoothing
  19. 19. Image ProcessingErosion and DilationUsed to diminish or accentuate featuresErode + Dilate = Removal of stray marks Erosion erode( src, erosion_dst, element ); Dilation dilate( src, dilation_dst, element );
  20. 20. Image ProcessingHistogram calculation
  21. 21. Image ProcessingHistogram equalisation – Improves contrastcvEqualizeHist( img, out );
  22. 22. Image ProcessingEdge detection
  23. 23. Image ProcessingSobel Edge Detector
  24. 24. Image ProcessingLaplace Edge Detector
  25. 25. Image ProcessingCanny Edge DetectorBest edge detector availableUses more advanced intensity gradient based methods
  26. 26. Feature DetectionThe following 3 are considered to be keypoints in an image1) Edges2) Corner (also known as interest points)3) Blobs (also known as regions of interest )Once the features have been found, these features are „described“. That is, the details around the keypoints are recorded.Later these descriptors are matched against incoming images.
  27. 27. Feature DetectionFeature Extraction: SURF, SIFT, BRIEFFeature Descriptors: SURF, SIFT, BRIEF, STARMatchers: FLANN, BruteForce
  28. 28. Thank You :) debayan@uberlabs.net

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