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lecture17 - EECS 442 Computer vision Detectors part II...

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EECS 442 – Computer vision Detectors part II Descriptors Some slides of this lectures are courtesy of prof F. Li, prof S. Lazebnik, and various other lecturers • Blob detectors • Invariance • Descriptors
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Goal: Identify interesting regions from the images (edges, corners, blobs…) Descriptors Matching / Indexing / Recognition e.g. SIFT
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• Repeatability – The same feature can be found in several images despite geometric and photometric transformations • Saliency – Each feature is found at an “interesting” region of the image • Locality – A feature occupies a “relatively small” area of the image;
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Repeatability Scale invariance Pose invariance •Rotation •Affine Illumination invariance
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• Saliency / •Locality /
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Harris Detector
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Detector Illumination Rotation Scale View point partial No No Harris corner Yes Invariance
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Extract useful building blocks: blobs
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Edge detection g dx d f f g dx d Source: S. Seitz Edge Derivative of Gaussian Edge = maximum of derivative
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Edge detection as zero crossing g dx d f 2 2 f g dx d 2 2 Second derivative of Gaussian (Laplacian) Edge = zero crossing of second derivative Edge Source: S. Seitz
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Edge detection as zero crossing edge edge
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