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CENG 710 Fundamentals of Autonomous Robotics

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CENG 710 Fundamentals of Autonomous Robotics

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    1. Scale Invarient Feature Transform 1/25 14 Dec 2005 Maya Çakmak CENG 710 Fundamentals of Autonomous Robotics Scale Invariant Feature Transform Maya Çakmak

    2. Scale Invarient Feature Transform 2/25 14 Dec 2005 Maya Çakmak VISION “the most powerful sense” Area: Computer/Robot Vision Sub-area: Object Recognition Problem: “Obtain a representation that allows us to find a particular object we've encountered before”

    3. Scale Invarient Feature Transform 3/25 14 Dec 2005 Maya Çakmak Key properties of a good feature: Highly distinctive Easy to extract Invariant, tolerant to changes Easy to match against a large database

    4. Scale Invarient Feature Transform 4/25 14 Dec 2005 Maya Çakmak SIFT SIFT is an approach for detecting and extracting local feature descriptors in which image content is transformed into local feature coordinates.

    5. Scale Invarient Feature Transform 5/25 14 Dec 2005 Maya Çakmak The Paper “Distinctive Image Features from Scale Invariant Key-points” International Journal of Computer Vision, 2004 International Conference of Computer Vision, 1999 David Lowe CS Department, Univ. of British Columbia

    6. Scale Invarient Feature Transform 6/25 14 Dec 2005 Maya Çakmak The method is: Invariant to: Image scaling Translation Rotation Partially invariant to: Illumination changes View points

    7. Scale Invarient Feature Transform 7/25 14 Dec 2005 Maya Çakmak Stages in SIFT Scale-space extrema detection Keypoint localization Orientation assignment Keypoint descriptor

    8. Scale Invarient Feature Transform 8/25 14 Dec 2005 Maya Çakmak Scale Space Extrema Extrema of difference-of-Gaussian (DoG) of image

    9. Scale Invarient Feature Transform 9/25 14 Dec 2005 Maya Çakmak Method to obtain DoG

    10. Scale Invarient Feature Transform 10/25 14 Dec 2005 Maya Çakmak Key Point Localization Find local minimum and maximum of DoG

    11. Scale Invarient Feature Transform 11/25 14 Dec 2005 Maya Çakmak For each candidate: Remove keypoints with low contrast (with value treshold) Remove responses along edges ( with principle curvatures)

    12. Scale Invarient Feature Transform 12/25 14 Dec 2005 Maya Çakmak Orientation Assignment For the selected keypoint, at the closest scale: compute a gradient orientation histogram determine dominant orientation

    13. Scale Invarient Feature Transform 13/25 14 Dec 2005 Maya Çakmak Scale Space Images

    14. Scale Invarient Feature Transform 14/25 14 Dec 2005 Maya Çakmak DoG Images

    15. Scale Invarient Feature Transform 15/25 14 Dec 2005 Maya Çakmak Keypoint Images

    16. Scale Invarient Feature Transform 16/25 14 Dec 2005 Maya Çakmak Effect of eliminations

    17. Scale Invarient Feature Transform 17/25 14 Dec 2005 Maya Çakmak Keypoint Descriptor

    18. Scale Invarient Feature Transform 18/25 14 Dec 2005 Maya Çakmak Matching SIFT features

    19. Scale Invarient Feature Transform 19/25 14 Dec 2005 Maya Çakmak Matching in different scales

    20. Scale Invarient Feature Transform 20/25 14 Dec 2005 Maya Çakmak Matching in different scales

    21. Scale Invarient Feature Transform 21/25 14 Dec 2005 Maya Çakmak Matching different view points

    22. Scale Invarient Feature Transform 22/25 14 Dec 2005 Maya Çakmak Matching in different illumination

    23. Scale Invarient Feature Transform 23/25 14 Dec 2005 Maya Çakmak Multiple object instances

    24. Scale Invarient Feature Transform 24/25 14 Dec 2005 Maya Çakmak Closing Comments SIFT features are reasonably invariant to rotation, scaling, and illumination changes We can use them for matching and object recognition among other things Robust to occlusion, as long as we can see at least 3 features from the object we can compute the location and pose Efficient on-line matching, recognition can be performed in close-to-real time (at least for small object databases)

    25. Scale Invarient Feature Transform 25/25 14 Dec 2005 Maya Çakmak References [1] Lowe, David : Object Recognition from Local Scale-Invariant Features, ICCV, 1999 and IJCV, 2004 [2] Lowe, David : CVPR 2003 Tutorial [3] Matlab SIFT toolbox tutorial [4] Computer Vision Lecture Notes, by Pinar Duygulu, Bilkent University, CS department.

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