Computer Vision Algorithms for R users

August 21, 2017
By

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Just before the summer holidays, BNOSAC presented a talk called Computer Vision and Image Recognition algorithms for R users at the UseR conference. In the talk 6 packages on Computer Vision with R were introduced in front of an audience of about 250 persons. The R packages we covered and that were developed by BNOSAC are:

  • image.CornerDetectionF9:  FAST-9 corner detection
  • image.CannyEdges: Canny Edge Detector
  • image.LineSegmentDetector: Line Segment Detector (LSD)
  • image.ContourDetector:  Unsupervised Smooth Contour Line Detection
  • image.dlib: Speeded up robust features (SURF) and histogram of oriented gradients (FHOG) features
  • image.darknet: Image classification using darknet with deep learning models AlexNet, Darknet, VGG-16, GoogleNet and Darknet19. As well object detection using the state-of-the art YOLO detection system

For those of you who missed this, you can still see the video of the presentation & view the pdf of the presentation below. The packages are open-sourced and made available at https://github.com/bnosac/image

If you have a computer vision endaveour in mind, feel free to get in touch for a quick chat. For those of you interested in following training on how to do image analysis, you can always register for our training on Computer Vision with R and Python here. More details on the full training program and training dates provided by BNOSAC: visit http://bnosac.be/index.php/training

{aridoc engine=”pdfjs” width=”100%” height=”450″}images/bnosac/blog/presentation-user2017.pdf{/aridoc}

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