'Gaussian noise' diaporamas de présentation

Digital Image Processing

Digital Image Processing. Chapter 5: Image Restoration. A Model of the Image Degradation/Restoration Process. Degradation Degradation function H Additive noise Spatial domain Frequency domain. Restoration. Noise Models. Sources of noise Image acquisition, digitization, transmission

1.37k views • 108 slides

Linear Filters

Linear Filters. T-11 Computer Vision University of Houston Christophoros Nikou. Images and slides from: James Hayes, Brown University, Computer Vision course Svetlana Lazebnik, University of North Carolina at Chapel Hill, Computer Vision course

919 views • 59 slides

Time series analysis

Time series analysis. Example. Objectives of time series analysis. Classical decomposition: An example. Transformed data. Trend. Residuals. Trend and seasonal variation. Objectives of time series analysis. Unemployment data. Trend. Trend plus seasonal variation.

957 views • 56 slides

WIRELINE CHANNEL ESTIMATION AND EQUALIZATION

WIRELINE CHANNEL ESTIMATION AND EQUALIZATION. Ph.D. Defense Biao Lu Embedded Signal Processing Laboratory The University of Texas at Austin Committee Members Prof. Brian L. Evans Prof. Alan C. Bovik Prof. Joydeep Ghosh Prof. Risto Miikkulainen Dr. Lloyd D. Clark. OUTLINE.

892 views • 48 slides

Adaptive Filtering of Raster Map Images

576 views • 48 slides

Linear filtering

545 views • 44 slides

Noise

Noise. Lecture 6. Definition Sources of noise Noise Calculations. Definition:. Electrical noise may be said to be the introduction of any unwanted energy, which tend to interfere with the proper reception and reproduction of transmitted signals. . Sources of noise. External Atmospheric

775 views • 35 slides

Decision Theory: Action Problems

Decision Theory: Action Problems. Decision theory goes Bad?. And thus the native hue of resolution Is sicklied o'er with the pale cast of thought, And enterprises of great pith and moment With this regard their currents turn awry, And lose the name of action.-- Shakespeare-Hamlet.

499 views • 34 slides

the RASNIK Alignment System

536 views • 34 slides

Fitting

472 views • 33 slides

Matched Filtering and Digital Pulse Amplitude Modulation (PAM)

Matched Filtering and Digital Pulse Amplitude Modulation (PAM). Outline. Transmitting one bit at a time Matched filtering PAM system Intersymbol interference Communication performance Bit error probability for binary signals Symbol error probability for M -ary (multilevel) signals

894 views • 32 slides

Fundamentals of Perceptual Audio Encoding

Fundamentals of Perceptual Audio Encoding. Craig Lewiston HST.723 Lab II 3/23/06. Goals of Lab. Introduction to fundamental principles of digital audio & perceptual audio encoding Learn the basics of psychoacoustic models used in perceptual audio encoding.

687 views • 30 slides

Digital Communication I: Modulation and Coding Course

Period 3 - 2007 Catharina Logothetis Lecture 3. Digital Communication I: Modulation and Coding Course. Last time we talked about:. Transforming the information source to a form compatible with a digital system Sampling Aliasing Quantization Uniform and non-uniform Baseband modulation

508 views • 22 slides

Chapter 11

Chapter 11. Signal Processing with Wavelets. Objectives. Define and illustrate the difference between a stationary and non-stationary signal. Describe the relationship between wavelets and sub-band coding of a signal using quadrature mirror filters with the property of perfect reconstruction.

466 views • 21 slides

Mining Historical Archives for Near-Duplicate Figures

Mining Historical Archives for Near-Duplicate Figures. Thanawin Rakthanmanon, Qiang Zhu, and Eamonn J. Keogh.

323 views • 21 slides

From Learning Models of Natural Image Patches to Whole Image Restoration

From Learning Models of Natural Image Patches to Whole Image Restoration. Daniel Zoran Interdisciplinary Center for Neural Computation Hebrew University of Jerusalem Yair Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Presented by Eric Wang

349 views • 14 slides

What to Do With Thousands of GPS Tracks

What to Do With Thousands of GPS Tracks. John Krumm, PhD Microsoft Research Redmond, WA. GPS Data. Microsoft Multiperson Location Survey (MSMLS). Garmin Geko 201 $115 10,000 point memory median recording interval 6 seconds 63 meters. 55 GPS receivers 227 subjects

210 views • 13 slides

What to Do With Thousands of GPS Tracks

What to Do With Thousands of GPS Tracks. John Krumm, PhD Microsoft Research Redmond, WA. GPS Data. Microsoft Multiperson Location Survey (MSMLS). Garmin Geko 201 $115 10,000 point memory median recording interval 6 seconds 63 meters. 55 GPS receivers 227 subjects

256 views • 13 slides

Why You Need to Measure Both BER and MER on QAM Digital Signals

811 views • 12 slides

EE 290P Project Proposal: Next-Generation Decoding Algorithms for BMI Systems

EE 290P Project Proposal: Next-Generation Decoding Algorithms for BMI Systems. Siddharth Dangi Suraj Gowda Paul Johnson. Preliminary work: Adaptive Kalman Filter. Dynamics of model. Variables. Kinematic state at time t. Firing rates at time t. Gaussian noise variables. State matrix.

232 views • 10 slides