'Smallest eigenvalue' diaporamas de présentation

Normalized Cuts and Image Segmentation

Normalized Cuts and Image Segmentation. Jianbo Shi and Jitendra Malik, Presented by: Alireza Tavakkoli. Image Segmentation. Image segmentation. How do you pick the right segmentation?. Bottom up segmentation: - Tokens belong together because they are locally coherent.

1.92k views • 54 slides

Segmentation using eigenvectors

901 views • 52 slides

Multi-Scale Surface Descriptors

627 views • 50 slides

3D Models and Matching

3D Models and Matching. representations for 3D object models particular matching techniques alignment-based systems appearance-based systems. GC model of a screwdriver. 3D Models. Many different representations have been used to model 3D objects.

534 views • 46 slides

C o l o ri n g graph powers; A Fourier approach

C o l o ri n g graph powers; A Fourier approach. N. Alon, I. Dinur, E. Friedgut, B. Sudakov. Traffic light. Whenever you change all the switches. ...the light changes!. How does that work?!. Maybe... the light depends on only one switch?. Weak graph products. Weak graph products.

438 views • 37 slides

Graph-based Segmentation

02/25/10. Graph-based Segmentation. Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem. Last class. Gestalt cues and principles of organization Mean-shift segmentation Good general-purpose segmentation method Generally useful clustering, tracking technique

533 views • 35 slides

Fitting

559 views • 34 slides

Probability Review

Probability Review. (many slides from Octavia Camps). Intuitive Development. Intuitively, the probability of an event a could be defined as:. Where N(a) is the number that event a happens in n trials. More Formal:. W is the Sample Space: Contains all possible outcomes of an experiment

506 views • 27 slides

Multidimensional Adaptive Testing with Optimal Design Criteria for Item Selection

363 views • 27 slides

Recursive Least-Squares (RLS) Adaptive Filters

Recursive Least-Squares (RLS) Adaptive Filters. Definition. With the arrival of new data samples estimates are updated recursively. Introduce a weighting factor to the sum-of-error-squares definition. two time-indices n: outer, i: inner. Weighting factor. Forgetting factor.

3.74k views • 21 slides

Quantum NP

Quantum NP. Dorit Aharonov & Tomer Naveh Presented by Alex Rapaport. Introduction. The goal is to define something like NP and NP completeness in the quantum world. Kitaev defines quantum analog of NP and a complete problem analog to SAT. MA. Definition:. x. V. y. MA.

350 views • 19 slides

VLSI Physical Design Automation

VLSI Physical Design Automation. Lecture 5. Circuit Partitioning (III) Spectral and Flow. Prof. David Pan dpan@ece.utexas.edu Office: ACES 5.434. Recap of what you have learned. KL Algorithm FM algorithm Variation and Extension Multilevel partition (hMetis) Simulated Annealing.

226 views • 14 slides

Simultaneous Equation Models class notes by Prof. Vinod all rights reserved

Simultaneous Equation Models class notes by Prof. Vinod all rights reserved. Marshallian Demand Supply . No equilibrium unless we consider both equations. Estimate simultaneously Two equation macro equilibrium. MPC overestimated even asymptotically T ? ?

329 views • 12 slides

The Limits of Adiabatic Quantum Algorithms

The Limits of Adiabatic Quantum Algorithms. Alper Sarikaya Advised by Prof. Dave Bacon Computer Science & Engineering Chemistry University of Washington Undergraduate Research Symposium May 15, 2009. Quantum Computing Theory Group: http://cs.washington.edu/homes/dabacon/qw/. Motivation.

239 views • 10 slides

Spectral Sequencing Based on Graph Distance

Spectral Sequencing Based on Graph Distance. Rong Liu, Hao Zhang, Oliver van Kaick {lrong, haoz, ovankaic} @ cs.sfu.ca. School of Computing Science, Simon Fraser University, Burnaby, Canada. Introduction. Problem.

156 views • 1 slides

Projected Hessian and quasi-Newton FWI

Yong Ma. Projected Hessian and quasi-Newton FWI. Sparse model space for projected Hessian. Eigenvector (largest eigenvalue ). Eigenvector (smallest eigenvalue ). Quasi-Newton FWI.

165 views • 1 slides