'Discrete convolution' diaporamas de présentation

Advanced Computer Graphics (Spring 2013)

Advanced Computer Graphics (Spring 2013). CS 283, Lecture 2: Sampling and Reconstruction Ravi Ramamoorthi. http:// inst.eecs.berkeley.edu /~cs283 /sp13. Some slides courtesy Thomas Funkhouser and Pat Hanrahan. Basics. First 3 lectures cover basic topics

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Foundations of Computer Graphics (Spring 2012)

Foundations of Computer Graphics (Spring 2012). http:// inst.eecs.berkeley.edu /~cs184. CS 184, Lectures 19: Sampling and Reconstruction. Acknowledgements: Thomas Funkhouser and Pat Hanrahan. Outline. Basic ideas of sampling, reconstruction, aliasing

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Lecture 22 Exemplary Inverse Problems including Filter Design

Lecture 22 Exemplary Inverse Problems including Filter Design. Syllabus.

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Advanced Computer Graphics

Advanced Computer Graphics. http:// www.cs.ucsd.edu /~ ravir. CSE 190 [ Winter 2016 ] , Lecture 4 Ravi Ramamoorthi. To Do. Assignment 1, Due Jan 29. Please START EARLY This lecture completes all the material you need. Outline. Implementation of digital filters

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Image processing

Image processing. Reading. Jain, Kasturi, Schunck, Machine Vision . McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. Image processing. An image processing operation typically defines a new image g in terms of an existing image f.

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Image processing

Image processing. Reading. Jain, Kasturi, Schunck, Machine Vision . McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. Image processing. An image processing operation typically defines a new image g in terms of an existing image f.

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Image processing

Image processing. Reading. Jain, Kasturi, Schunck, Machine Vision . McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. Image processing. An image processing operation typically defines a new image g in terms of an existing image f.

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Convolution

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FFT Convolution

FFT Convolution. Michael Phipps Vallary S. Bhopatkar. Convolution theorem. Convolution theorem for continuous case: h(t) and g(t) are two functions and H(f) and G(f) are their corresponding Fourier Transform, then convolution is defined as

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2D Fourier transfor m

2D Fourier transfor m. 2D Fourier transfor m is separable. Visualisation. Amplitud e Phase. B asis fun ctions of 2D FT. Real part, u=v. Rectangular pulse in 2D. Other important functions. Discrete convolution theorem. ... holds for periodic convolution only!.

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