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Advanced Computer Vision

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  1. Advanced Computer Vision Lecture 03 Roger S. Gaborski Roger S. Gaborski

  2. Video Lecture • • Paper: VLFeat-An open and portable library of computer vision algorithms • Roger S. Gaborski

  3. Object Recognition • Issues: • Viewpoint • Scale • Deformable vs. rigid • Clutter • Occlusion • Intra class variability Roger S. Gaborski

  4. Goal • Locate all instances of automobiles in a cluttered scene Roger S. Gaborski

  5. Acknowledgements • Students (Thesis in RIT Library): • Tim Lebo • Dan Clark • Images used in presentation: • ETHZ Database, UIUC Database Roger S. Gaborski

  6. Object Recognition Approaches • For specific object class: • Holistic • Model whole object • Parts based • Simple parts • Geometric relationship information • We could use a similar approach to match patches representing different image categories (‘sand’ patches located on lower half of beach scenes) Roger S. Gaborski

  7. Training Images and Segmentation Roger S. Gaborski

  8. Implicit Shape Model • Patches – local appearance prototypes • Spatial relationship – where the patch can be found on the object • For a given class w: ISM(w) = (Iw ,Pw ) where Iw is the codebook containing the patches and Pw is the probability distribution that describes where the patch is found on the object • How do we find ‘interesting’ patches? Roger S. Gaborski

  9. Harris Point Operator Roger S. Gaborski

  10. Harris Points Roger S. Gaborski

  11. Segmented Training Mask Segmented mask ensures only patches containing valid car regions are selected A corresponding segmentation patch is also extracted Roger S. Gaborski

  12. Selected Patches Roger S. Gaborski

  13. How is spatial information represented? • Estimate the center of the object using the centroid of the segmentation mask • Displacement between: • Center of patch • Centroid of segmentation mask Roger S. Gaborski

  14. Individual Patch and Displacement Information Roger S. Gaborski

  15. Typical Training Example Roger S. Gaborski

  16. Typical Training Example Roger S. Gaborski

  17. Extracted Training Patches Roger S. Gaborski

  18. Cluster Patches • Many patches will be visually similar • Normalized Grayscale Correlation is used to cluster patches • All patches within a certain neighborhood defined by the NGC are grouped together • The representative patch is determined by mean of the patches • The geometric information for each patch in the cluster is assigned to the representative patch Roger S. Gaborski

  19. Patches Roger S. Gaborski

  20. Wheel Patch Example Roger S. Gaborski

  21. Clusters Opportunity for better clustering method Roger S. Gaborski

  22. Clusters Roger S. Gaborski

  23. Roger S. Gaborski

  24. Object Detection • Harris point operator to find interesting points • Extract patches • Match extracted patches with model patches • Spatial information predicts center of object • Create voting space Roger S. Gaborski

  25. Ideal Voting Space Example Roger S. Gaborski

  26. Multiple Votes Multiple geometric interpretations Roger S. Gaborski

  27. Resolving False Detections Roger S. Gaborski

  28. Localization: Find Corners Roger S. Gaborski

  29. Localization: Model Matching Roger S. Gaborski

  30. Localization: Find Corners Roger S. Gaborski

  31. Model Matching Roger S. Gaborski

  32. Spatial Activation(Hough Space) 9000 different locations Roger S. Gaborski

  33. Hypothesis Candidates 16 candidate locations Roger S. Gaborski

  34. Hypothesis Candidates Roger S. Gaborski

  35. References SEE RESOURCES ON COURSE WEB PAGE: Timothy Lebo and Roger Gaborski, “A Shape model with Coactivation Networks for Recognition and Segmentation,” Eighth International conference on Signal and image Processing, Honolulu, HI. August 2006. Timothy Lebo, “Guiding Object Recognition: A Shape Model with Co-activation Networks,” MS Thesis, RIT, 2005. Daniel Clark, “Object Detection and Tracking using a Parts Based Approach,” MS Thesis, RIT, 2005. Roger S. Gaborski

  36. References Bastian Leibe, Ales Leonardis, and Bernt Schiele, “Combined object categorization and segmentation with an implicit shape model,” ECCV’04 Workshop onStatistical Learning in Computer Vision, May 2004. Shivani Agarwal, Aatif Awan, and Dan Roth, “Learning to detect objects in images via a sparse, part-based representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475–1490, 2004. Roger S. Gaborski

  37. Roger S. Gaborski

  38. Voting Space Roger S. Gaborski

  39. Roger S. Gaborski

  40. Model Patches Selected Roger S. Gaborski

  41. True Object Patches Roger S. Gaborski

  42. Identified Objects Roger S. Gaborski

  43. Research Topics • Scene Categories • MATLAB TOOLKIT: Roger S. Gaborski

  44. Reference Examples Recognizing Indoor Scenes AriadnaQuattoni and Antonio Torralba ( Building the gist of a scene: the role of global image features in recognition AudeOliva and Antonio Torralba Objects as Attributes for Scene Classi¯cation Li-Jia Li, Hao Su, Yongwhan Lim, Li Fei-Fei ( See references for each paper Roger S. Gaborski

  45. Modeling the shape of the scene: a holistic representation of the spatial envelope • SIFT flow: dense correspondence across difference scenes Roger S. Gaborski

  46. Learning and Recognizing Visual Object Categories • Deep Learning: Visual Perception with Deep Learning Roger S. Gaborski

  47. Typical Images coast_cdmc976.jpg coast_n708004.jpg coast_n384026.jpg coast_n739047.jpg forest_for142.jpg forest_for42.jpg forest_for38.jpg forest_for58.jpg tallbuilding_a487092.jpg tallbuilding_a803053.jpg tallbuilding_art1350.jpg tallbuilding_art1506.jpg Roger S. Gaborski

  48. Coast Images Roger S. Gaborski

  49. Forest Roger S. Gaborski

  50. Tall Buildings Roger S. Gaborski