'Shift invariant' diaporamas de présentation

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ELEN E4810: Digital Signal Processing Topic 2: Time domain

ELEN E4810: Digital Signal Processing Topic 2: Time domain. Discrete-time systems Convolution Linear Constant-Coefficient Difference Equations (LCCDEs) Correlation. DT System. x [ n ]. y [ n ]. 1/ M. x [ n ]. z -1. 1/ M. y [ n ]. x [ n -1]. +. z -1. 1/ M. x [ n -2].

837 views • 59 slides

Frame-Level Pipelined Motion Estimation Array Processor

Frame-Level Pipelined Motion Estimation Array Processor. Surin Kittitornkun and Yu Hen Hu. IEEE Trans. on, for Video Tech., Vol. 11, NO.2 FEB, 2001. OUTLINE. Methodology for VLSI Array Processors Design An Example on Frame Level Block Matching Algorithm. Design Levels. Sequential Algorithm

387 views • 27 slides

Large Lump Detection by SVM

Large Lump Detection by SVM. Sharmin Nilufar Nilanjan Ray. Outline. Introduction Proposed classification method Scale space analysis of LLD images Feature for classification Experiments and results Conclusion. Introduction.

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Comp 665

Comp 665. Convolution. Questions?. Ask here first. Likely someone else has the same question. Modeling the imaging process. We know how to compute where points in the world will map on the image plane Now, how will they be changed?. Impulse Response Function. Point Spread Function

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Linear Shift-Invariant Systems

Linear Shift-Invariant Systems. Linear. If x(t) and y(t) are two input signals to a system, the system is linear if H[a*x(t) + b*y(t)] = aH[x(t)] + bH[y(t)] Where H specifies the transformation performed on the signal by the system. Shift Invariant.

1.23k views • 15 slides

Redundant Wavelet Transform and its Application in Denoising and Predication

Redundant Wavelet Transform and its Application in Denoising and Predication. Jiajun Gu May 15, 2003. Outline:. Definition Redundant Wavelet Transform Relationship with Discrete Wavelet Transform Relationship with Continuous Wavelet Transform Application Denoising Predication.

344 views • 14 slides

An Improved Version of the Inverse Hyperanalytic Wavelet Transform

An Improved Version of the Inverse Hyperanalytic Wavelet Transform. Ioana Firoiu, Alexandru Isar, Jean-Marc Boucher. Introduction. Wavelet techniques based on the Discrete Wavelet Transform (DWT) Advantages Sparsity of coefficients Disadvantages

340 views • 12 slides

An Improved Version of the Inverse Hyperanalytic Wavelet Transform

An Improved Version of the Inverse Hyperanalytic Wavelet Transform. Ioana Firoiu, Alexandru Isar, Jean-Marc Boucher. Introduction. Wavelet techniques based on the Discrete Wavelet Transform (DWT) Advantages Sparsity of coefficients Disadvantages

266 views • 12 slides