copyright Robert J. Marks II

Slides:



Advertisements
Similar presentations
Image Processing Lecture 4
Advertisements

Chapter 3 Image Enhancement in the Spatial Domain.
Copyright R.J. Marks II ECE 5345 Ergodic Theorems.
Data preprocessing before classification In Kennedy et al.: “Solving data mining problems”
Digital Image Processing
ECE 472/572 - Digital Image Processing
Image (and Video) Coding and Processing Lecture 5: Point Operations Wade Trappe.
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
Continuous Random Variable (2). Families of Continuous Random Variables Uniform R.V. Exponential R.V. Gaussian R.V.
EEE 498/591- Real-Time DSP1 What is image processing? x(t 1,t 2 ) : ANALOG SIGNAL x : real value (t 1,t 2 ) : pair of real continuous space (time) variables.
CS 351/ IT 351 Modelling and Simulation Technologies Random Variates Dr. Jim Holten.
Stochastic Resonance n Adding Noise to a signal can help in its detection. n Just the right amount of noise must be added (resonance) n SR in Nature –Periodic.
Low Level Statistics and Quality Control Javier Cabrera.
Ya Bao Fundamentals of Communications theory1 Random signals and Processes ref: F. G. Stremler, Introduction to Communication Systems 3/e Probability All.
3D CT Image Data Visualize Whole lung tissues Using VTK 8 mm
Random Variables.
ELE 745 – Digital Communications Xavier Fernando
HELSINKI UNIVERSITY OF TECHNOLOGY LABORATORY OF COMPUTER AND INFORMATION SCIENCE NEURAL NETWORKS RESEACH CENTRE Variability of Independent Components.
VEHICLE NUMBER PLATE RECOGNITION SYSTEM. Information and constraints Character recognition using moments. Character recognition using OCR. Signature.
Prof. SankarReview of Random Process1 Probability Sample Space (S) –Collection of all possible outcomes of a random experiment Sample Point –Each outcome.
Copyright Robert J. Marks II ECE 5345 Random Processes - Stationary Random Processes.
Introduction.
Stochastic Resonance and its implications for the brain Jaeseung Jeong, Ph.D Department of Bio and Brain Engineering, KAIST.
Work-Life Balance Focused for Graduate Students Reception and Discussion Wednesday, December 5 2:30 - 4:30 p.m. Neill 216 Dr. Rachel Kuske University of.
Copyright Robert J. Marks II ECE 5345 Multiple Random Variables: A Gambling Sequence.
Chapter 3: Image Restoration Introduction. Image restoration methods are used to improve the appearance of an image by applying a restoration process.
Data Hiding in Image and Video Part I: Fundamental Issues and Solutions ECE 738 Class Presentation By Tanaphol Thaipanich
Copyright © 2010 Pearson Addison-Wesley. All rights reserved. Chapter 6 Some Continuous Probability Distributions.
Computer Vision Group Prof. Daniel Cremers Autonomous Navigation for Flying Robots Lecture 6.1: Bayes Filter Jürgen Sturm Technische Universität München.
Spatial Filtering (Applying filters directly on Image) By Engr. Muhammad Saqib.
Computational Biology, Part 22 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Slide 1 NATO UNCLASSIFIEDMeeting title – Location - Date Satellite Inter-calibration of MODIS and VIIRS sensors Preliminary results A. Alvarez, G. Pennucci,
STATISTIC & INFORMATION THEORY (CSNB134) MODULE 7B PROBABILITY DISTRIBUTIONS FOR RANDOM VARIABLES ( POISSON DISTRIBUTION)
CS 351/ IT 351 Modeling and Simulation Technologies Review ( ) Dr. Jim Holten.
Geology 6600/7600 Signal Analysis 09 Sep 2015 © A.R. Lowry 2015 Last time: Signal Analysis is a set of tools used to extract information from sequences.
AUTOMATIC CONTROL THEORY II Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Compression and Security of Surveillance Videos Exercise 6 – Shot Change Detection M 陳威佑.
基 督 再 來 （一）. 經文： 1 你們心裡不要憂愁；你們信神，也當信我。 2 在我父的家裡有許多住處；若是沒有，我就早 已告訴你們了。我去原是為你們預備地去 。 3 我 若去為你們預備了地方，就必再來接你們到我那 裡去，我在 那裡，叫你們也在那裡， ] ( 約 14 ： 1-3)
INTRODUCTION. Electrical and Computer Engineering  Concerned with solving problems of two types:  Production or transmission of power.  Transmission.
What is the average rate of change of the function f (x) = 8 x - 7 between x = 6 and x = 7? Select the correct answer:
Copyrights apply.
- photometric aspects of image formation gray level images
Maximally Stable Extremal Regions
Suppose you roll two dice, and let X be sum of the dice. Then X is
Given that, {image} {image} Evaluate the limit: {image} Choose the correct answer from the following: {image}
Слайд-дәріс Қарағанды мемлекеттік техникалық университеті
.. -"""--..J '. / /I/I =---=-- -, _ --, _ = :;:.
Stochastic Resonance Adding Noise to a signal can help in its detection. Just the right amount of noise must be added (resonance) SR in Nature Periodic.
Introduction to Digital Image Analysis Part II: Image Analysis
EE 505 Multiple Random Variables (cont)
II //II // \ Others Q.
ВОМР Подмярка 19.2 Възможности за финансиране
Споразумение за партньорство
I1I1 a 1·1,.,.,,I.,,I · I 1··n I J,-·
Chapter 6 Some Continuous Probability Distributions.
copyright Robert J. Marks II
Non-parametric Filters: Particle Filters
ECE 5345 Stochastic Processes
Chapter 3 : Random Variables
Signal Detection & Processing
Multiple Choice Quiz.
Non-parametric Filters: Particle Filters
Arjun Raj, Alexander van Oudenaarden  Cell 
EE 5345 Multiple Random Variables 2-D RV’s Conditional RV’s
Automated Detection and Analysis of Ca2+ Sparks in x-y Image Stacks Using a Thresholding Algorithm Implemented within the Open-Source Image Analysis Platform.
Distribution-free Monte Carlo for population viability analysis
. '. '. I;.,, - - "!' - -·-·,Ii '.....,,......, -,
ECE 5345 Gaussian Random Processes copyright R.J. Marks II.
copyright Robert J. Marks II
Wednesday After Lunch: Add randomness to the Flowers Model
Presentation transcript:

copyright Robert J. Marks II ECE 5345 Multiple Random Variables: Stochastic Resonance copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance Stochastic resonance is loosely defined as the phenomena in a detection process wherein the addition of just the right amount and type of noise improves performance. Too little or too much noise result in degraded performance. copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance Too Little Noise Just Right! Too Much Noise copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance Dynamic Stochastic resonance Squint Your Eyes Original Image Too Little Noise Just Right! Too Much Noise http://cialab.ee.washington.edu/Presentations/StochasticResonance/home.html copyright Robert J. Marks II

Binary Stochastic Process Stochastic Resonance Binary Stochastic Process Signal Threshold Unit Step Noise copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance: The expected value is the signal run through a nonlinearity specified by the CDF of the noise. copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance in Uniform Noise The normalized histogram for E[Z(t)] is where copyright Robert J. Marks II

copyright Robert J. Marks II Stochastic Resonance in Uniform Noise Correct value of  gives “exact” results! copyright Robert J. Marks II

copyright Robert J. Marks II uniform noise: - to  about 0.5 copyright Robert J. Marks II

copyright Robert J. Marks II Averaging copyright Robert J. Marks II

copyright Robert J. Marks II

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2025 SlidePlayer.com. Inc. All rights reserved.