'Discrete convolution' presentation slideshows

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

716 views • 60 slides

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

606 views • 60 slides

Lecture 22 Exemplary Inverse Problems including Filter Design

Lecture 22 Exemplary Inverse Problems including Filter Design. Syllabus.

708 views • 52 slides

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

447 views • 44 slides

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.

585 views • 34 slides

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.

623 views • 34 slides

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.

347 views • 34 slides

Convolution

353 views • 28 slides

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

410 views • 16 slides

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!.

385 views • 14 slides

259 views • 10 slides