'Perfect reconstruction' diaporamas de présentation

Signal reconstruction from multiscale edges

Signal reconstruction from multiscale edges. A wavelet based algorithm. Algorithm. Decomposition. Input. Discrete Wavelet Transform. Save edges e.g. local extrema. Reconstruction. local extrema. Find approximation. Inverse Wavelet Transform. Output. contained two bugs.

1.16k views • 93 slides

Signal reconstruction from multiscale edges

Signal reconstruction from multiscale edges. A wavelet based algorithm. Algorithm. Decomposition. Input. Discrete Wavelet Transform. Save edges e.g. local extrema. Reconstruction. local extrema. Find approximation. Inverse Wavelet Transform. Output. contained two bugs.

934 views • 93 slides

Meshless wavelets and their application to terrain modeling

Meshless wavelets and their application to terrain modeling. A DARPA GEO* project Jack Snoeyink, Leonard McMillan, Marc Pollefeys, Wei Wang (UNC-CH) Charles Chui, Wenjie He (UMSL). Outline. Project Team, Motivation, & Objectives Meshless wavelets

683 views • 57 slides

DSP-CIS Chapter-8: Maximally Decimated PR-FBs

DSP-CIS Chapter-8: Maximally Decimated PR-FBs. Marc Moonen Dept. E.E./ESAT, KU Leuven marc.moonen@esat.kuleuven.be www.esat.kuleuven.be / scd /. Part-III : Filter Banks. : Preliminaries Filter bank (FB) set-up and applications Perfect reconstructio n (PR) problem

545 views • 40 slides

Digital Signal Processing II Lecture 6: Maximally Decimated Filter Banks

Digital Signal Processing II Lecture 6: Maximally Decimated Filter Banks. Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.be homes.esat.kuleuven.be/~moonen/. Part-II : Filter Banks. : Preliminaries Applications

647 views • 38 slides

Digital Signal Processing II Lecture 7: Modulated Filter Banks

Digital Signal Processing II Lecture 7: Modulated Filter Banks. Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.be homes.esat.kuleuven.be/~moonen/. Part-II : Filter Banks. : Preliminaries Applications

959 views • 30 slides

Digital Signal Processing II Lecture 7: Modulated Filter Banks

Digital Signal Processing II Lecture 7: Modulated Filter Banks. Marc Moonen Dept. E.E./ESAT, K.U.Leuven marc.moonen@esat.kuleuven.be homes.esat.kuleuven.be/~moonen/. Part-II : Filter Banks. : Preliminaries Filter bank set-up and applications

408 views • 30 slides

SWE 423: Multimedia Systems

SWE 423: Multimedia Systems. Chapter 7: Data Compression (6). Outline. JPEG Introduction JPEG Requirements JPEG Modes and Procedure. Introduction. JPEG (Joint Photographic Experts Group) is the result of a joint project between ISO and CCITT

479 views • 25 slides

Team Members, Pon Marimuthu S

An Efficient Image Edge Detection Method Based on Non-Perfect Reconstruction Biorthogonal wavelet. Team Members, Pon Marimuthu S

255 views • 25 slides

Multi-Resolution Analysis

Multi-Resolution Analysis. Non-stationary Property of Natural Image. Pyramidal Image Structure. Z-transform. The z-transform is the discrete time version of Laplace transform. Given a sequence {x(n)}, its z-transform is: In particular,. Z-Transform and Fourier Transform.

510 views • 23 slides

W.A.V.S. Compression

W.A.V.S. Compression. Alex Chen Nader Shehad Aamir Virani Erik Welsh. Overview. Approach Psychoacoustic Modeling Filter Banks Quantization Demonstration Results Further Research. Approach. Encoding:. Filter Banks. Quantization. Input. Encoded Signal. Psychoacoustic Model.

386 views • 21 slides

FFT-based filtering and the Short-Time Fourier Transform (STFT)

FFT-based filtering and the Short-Time Fourier Transform (STFT). R.C. Maher ECEN4002/5002 DSP Laboratory Spring 2003. Using the FFT for DSP.

858 views • 16 slides

Analog/Digital Coding

Analog/Digital Coding. Bobby Geevarughese ECE-E443 4/12/05. Overview. Analog Signals Digital Signals A/D Coding D/A Coding. Analog Signals. Sound carried as pattern of pressure waves Change in pressure is measured in each instant and voltage will vary in same way.

407 views • 15 slides

A VLSI Architecture for the 2-D Discrete Wavelet Transform

547 views • 15 slides

Tomographic Approach for Sampling Multidimensional Signals with Finite Rate of Innovation

Tomographic Approach for Sampling Multidimensional Signals with Finite Rate of Innovation. Pancham Shukla and Pier Luigi Dragotti Communications and Signal Processing Group Electrical and Electronic Engineering Department Imperial College, London SW7 2AZ, UK

282 views • 12 slides

CV in a Nutshell (||)

CV in a Nutshell (||). Yi Li http://users.cecs.anu.edu.au/~ yili/CVinNutshell.htm. Outline. Paper discussion Image Processing (overview) Diffusion Process Image Processing (filtering) Filter Banks Image Processing (filtering) Noise Removal. Paper discussion.

237 views • 11 slides

Feb. 2, 2010

Feb. 2, 2010. Introduction to Lifting Wavelet Transform (computationally efficient filterbank implementation) and Homework 3. Lazy Wavelet Transform-I. x 1 : even samples of x[n] and x 2 = x odd [n-1] x'[n] = x[n-1] It is lazy because it does not filter the input signal.

216 views • 8 slides

Image Interpolation

Image Interpolation. Signals and image processing by computer Winter 2012-13 Yael Erez. Image Interpolation. Sampled Image: Reconstructed image by interpolation:. Image Interpolation. Separable Interpolation Kernels Shannon Kernel

391 views • 5 slides