freeman algorithms and applications in computer vision lihi zelnik-manor [email protected]...

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Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor [email protected] Lecture 5: Pyramids

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Page 1: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Algorithms and Applications in Computer Vision

Lihi [email protected] 5: Pyramids

Page 2: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Image information occurs at all spatial scales

Page 3: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Image pyramids

• Gaussian pyramid• Laplacian pyramid

Page 4: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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The Gaussian pyramid

• Smooth with gaussians, because– a gaussian*gaussian=another gaussian

• Gaussians are low pass filters, so representation is redundant.

Page 5: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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The computational advantage of pyramids

http://www-bcs.mit.edu/people/adelson/pub_pdfs/pyramid83.pdf

Page 6: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freemanhttp://www-bcs.mit.edu/people/adelson/pub_pdfs/pyramid83.pdf

Page 7: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Page 8: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Gaussian pyramids used for

• up- or down- sampling images.• Multi-resolution image analysis– Look for an object over various spatial scales– Coarse-to-fine image processing: form blur

estimate or the motion analysis on very low-resolution image, upsample and repeat. Often a successful strategy for avoiding local minima in complicated estimation tasks.

Page 9: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Image pyramids

• Gaussian• Laplacian

Page 10: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

The Laplacian Pyramid

• Synthesis– Compute the difference between upsampled

Gaussian pyramid level and Gaussian pyramid level.

– band pass filter - each level represents spatial frequencies (largely) unrepresented at other level.

Page 11: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Laplacian pyramid algorithm

Page 12: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Showing, at full resolution, the information captured at each level of a Gaussian (top) and Laplacian (bottom) pyramid.

http://www-bcs.mit.edu/people/adelson/pub_pdfs/pyramid83.pdf

Page 13: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Gaussian pyramid

Page 14: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman

Laplacian pyramid

Page 15: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Why use these representations?

• Handle real-world size variations with a constant-size vision algorithm.

• Remove noise• Analyze texture• Recognize objects• Label image features

Page 16: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman http://web.mit.edu/persci/people/adelson/pub_pdfs/RCA84.pdf

Page 17: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman http://www.hackerfactor.com/blog/index.php?/archives/440-The-Perfect-Blend.html

Page 18: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

Freeman http://www.hackerfactor.com/blog/index.php?/archives/440-The-Perfect-Blend.html

Page 19: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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Slide Credits

• Trevor Darrell• Bill Freeman• and others, as noted…

Page 20: Freeman Algorithms and Applications in Computer Vision Lihi Zelnik-Manor lihi@ee.technion.ac.il Lecture 5: Pyramids

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More on statistics of natural scenes• Olshausen and Field:

– Natural Image Statistics and Efficient Coding, https://redwood.berkeley.edu/bruno/papers/stirling.ps.Z

– Relations between the statistics of natural images and the response properties of cortical cells.

– http://redwood.psych.cornell.edu/papers/field_87.pdf

• Aude Olivia:– http://cvcl.mit.edu/SUNSlides/9.912-CVC-ImageAnal

ysis-web.pdf