Image Processing with OpenCV
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Image Processing with OpenCV

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At IP University, Dwarka

At IP University, Dwarka

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Image Processing with OpenCV Image Processing with OpenCV Presentation Transcript

  • Image Processing with OpenCV Debayan Banerjee Co-founder, Uberlabs
  • 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“
  • Examples Smoothing
  • ExamplesErosion ↔ Dilation
  • Examples Edge detection
  • ExamplesHough line transform
  • ExamplesFace detection
  • Basic ConceptsAn image is a matrix
  • Basic ConceptsA colour image has 3 2-d matrices for R, G , B
  • Basic conceptsExample
  • Basic operations: OpenCVReading and displaying images
  • Basic operations: OpenCVWriting images
  • Core module: OpenCVAccessing individual pixels
  • Core module: OpenCVContrast and Brightness adjustment g(x) = a f(x) + b a = Contrast parameter b = Brightness parameter
  • Core module: OpenCVContrast and Brightness example a =2.2 b=50
  • Core module: OpenCVDrawing functionsLinesCirclesEllipsesPolygon
  • 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 );
  • Image Processing Smoothing
  • 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 );
  • Image ProcessingHistogram calculation
  • Image ProcessingHistogram equalisation – Improves contrastcvEqualizeHist( img, out );
  • Image ProcessingEdge detection
  • Image ProcessingSobel Edge Detector
  • Image ProcessingLaplace Edge Detector
  • Image ProcessingCanny Edge DetectorBest edge detector availableUses more advanced intensity gradient based methods
  • 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.
  • Feature DetectionFeature Extraction: SURF, SIFT, BRIEFFeature Descriptors: SURF, SIFT, BRIEF, STARMatchers: FLANN, BruteForce
  • Thank You :) debayan@uberlabs.net