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Computer Vision Techniques for Underwater Navigation

Computer Vision Techniques for Underwater Navigation. Chris Barngrover CSE 291. May 5, 2010. Research Motivation. Doppler Velocity Logger SONAR Cameras. Specific Motivation. AUVSI & ONR’s 13 th Annual AUV Competition. TRANSDEC. Research Goal. Detect and Classify Objects Buoy Pipe.

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Computer Vision Techniques for Underwater Navigation

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  1. Computer Vision Techniques for Underwater Navigation Chris Barngrover CSE 291 May 5, 2010

  2. Research Motivation • Doppler Velocity Logger • SONAR • Cameras

  3. Specific Motivation • AUVSI & ONR’s 13th Annual AUV Competition TRANSDEC

  4. Research Goal • Detect and Classify Objects • Buoy • Pipe

  5. The Stingray Cameras Frame Grabber Processor

  6. Computer Vision • Labeling Examples

  7. Computer Vision • HSV Classifier • Hue – Saturation – Value • RGB is lighting dependant

  8. Computer Vision • Boosting Algorithms • JBoost

  9. Computer Vision • Binary Image

  10. Computer Vision

  11. Buoy Detection • Detect & Classify • Determine Center Location

  12. Buoy Detection • Baseline Algorithm • HSV Range • Misses Reflection • Noise

  13. Buoy Detection • Boosting Benefits • HSV Classifier • Robust Scoring per pixel • Reduced Noise

  14. Buoy Detection • Opening • Reduces Noise • Erosion then Dilation

  15. Buoy Detection • Closing • Fills holes • Dilation then Erosion

  16. Buoy Detection • Convex Hull • Closes edges

  17. Buoy Detection • Center Estimation • Centroids of Blobs • Largest Area Wins • Quality of Classifier

  18. Buoy Detection • Hybrid Boosting • TRANSDEC & Pool • Separate Decision Trees • Additive Scoring

  19. Buoy Detection • Reflection Problem • Larger Reflection Blob • Look at 2nd Largest

  20. Buoy Detection Baseline Metrics Final Algorithm Metrics

  21. Pipe Detection • Detect & Classify • Determine Center Location • Determine Bearing

  22. Pipe Detection • Baseline Algorithm • HSV Range • Finds Pipe Generally • Lots of Noise

  23. Pipe Detection • Boosting • HSV Classifier • Post Processing • Opening • Closing • Convex Hull • Smooth

  24. Pipe Detection • Edge Detection • Blob Perimeter • Canny Algorithm

  25. Pipe Detection • Hough Transform • Standard (SHT) • Probabilistic (PHT) • Multiple lines per edge

  26. Pipe Detection • Collinear Lines • Merge semi-collinear • Error from best-fit

  27. Pipe Detection • Parallel Lines • Remove solo lines

  28. Pipe Detection • Two Pipes • Match lines with center of pipe

  29. Pipe Detection • Two Line Pairs • Choose pair closest to the center

  30. Pipe Detection Baseline Metrics Final Algorithm Metrics

  31. Future Efforts • Fish Detection • Quagga Mussels • Mine Detection

  32. Related Work • Perceptual Robotics Laboratory @ UMich • Visually Augmented Navigation • Autonomous Ship Hull Inspection • Koch Lab @ Cal Tech • Automated Event Detection in Underwater Video • Singh’s Lab @ Woods Hole • Underwater Photo Mosaicing

  33. Questions?

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