Fourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase
|
|
- Oscar Charles
- 6 years ago
- Views:
Transcription
1 Fourier Transform
2 Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2
3
4 1 sin sin 3 1 sin sin sin 5 1 sin sin sin 5 1 sin 7 7 4
5 Fourier Transform Input: infinite periodic signal Output: set of sine and cosine waves which together provide the input signal 5
6 Fourier Transform Digital Signals Hardly periodic Never infinite 6
7 Fourier Transform in 1D 7
8 Representation in Both Domains 2 Frequency Domain Amplitude Frequency Time Domain Phase 0 Frequency 8
9 Discrete Fourier Transform DFT decomposes x into 1cosine and sine waves Each of a different frequency 9
10 DFT - Rectangular Representation Decomposition of the time domain signal x to the frequency domain and 10
11 Polar Notation Sine and cosine waves are phase shifted versions of each other Amplitude Phase 11
12 Polar Representation 12
13 Polar Representation Unwrapping of phase 13
14 Properties Homogeneity Additivity 14
15 Properties Linear phase shift 15
16 Symmetric Signals Symmetric signal always has zero phase 16
17 Symmetric Signals Frequency response and circular movement 17
18 Amplitude Modulation 18
19 Periodicity of Frequency Domain Amplitude Plot 19
20 Periodicity of Frequency Domain Phase Plot 20
21 Aliasing 21
22 Sampling Frequency Domain Convolution -f s 2 f s 2 -f s f s 2 f s 3 f s -f s f s 2 f s 3 f s
23 Reconstruction Frequency Domain -f s f s 2 f s 3 f s -f s 2 f s 2 Multiplication -f s 2 f s 2
24 Reconstruction (Wider Kernel) -f s f s 2 f s 3 f s -f s 2 f s 2 PIXELIZATION: Lower frequency aliased as high frequency -f s 2 f s 2
25 Reconstruction (Narrower Kernel) -f s f s 2 f s 3 f s -f s 2 f s 2 BLURRING: Removal of high frequencies -f s 2 f s 2
26 Aliasing artifacts (Right Width)
27 Wider Spots (Lost high frequencies)
28 Narrow Width (Jaggies, insufficient sampling)
29 DFT extended to 2D : Axes Frequency Only positive Orientation 0 to 180 Repeats in negative frequency Just as in 1D
30 Example
31 How it repeats? Just like in 1D Even function for amplitude Odd function for phase For amplitude Flipped on the bottom
32 Why all the noise? Values much bigger than 255 DC is often 1000 times more than the highest frequencies Difficult to show all in only 255 gray values
33 Mapping Numerical value = i Gray value = g Linear Mapping is g = ki Logarithmic mapping is g = k log (i) Compresses the range Reduces noise May still need thresholding to remove noise
34 Example Original DFT Magnitude In Log scale Post Thresholding
35 Low Pass Filter Example
36 Additivity + Inverse DFT =
37 Nuances
38
39
40 Rotation What is this about?
41 X
42 X
43 More examples: Blurring Note energy reduced at higher frequencies What is direction of blur? Horizontal Noise also added DFT more noisy
44 More examples: Edges Two direction edges on left image Energy concentrated in two directions in DFT Multi-direction edges Note how energy concentration synchronizes with edge direction
45 More examples: Letters DFTs quite different Specially at low frequencies Bright lines perpendicular to edges Circular segments have circular shapes in DFT
46 More examples: Collections Concentric circle Due to pallets symmetric shape DFT of one pallet Similar Coffee beans have no symmetry Why the halo? Illumination
47 More examples: Natural Images Natural Images Why the diagonal line in Lena? Strongest edge between hair and hat Why higher energy in higher frequencies in Mandril? Hairs
48 More examples Spatial Repeatation makes perfect periodic signal Therefore perfect result perpendicular to it Frequency
49 More examples Spatial Just a gray telling all frequencies Why the bright white spot in the center? Frequency
50 Amplitude How much details? Sharper details signify higher frequencies Will deal with this mostly 50
51 Cheetah 51
52 Magnitude 52
53 Phase 53
54 Zebra 54
55 Magnitude 55
56 Phase 56
57 Reconstruction Cheetah Magnitude Zebra Phase 57
58 Reconstruction Zebra magnitude Cheetah phase 58
59 Uses Notch Filter 59
60 Uses
61 Smoothing Box Filter 61
62 Smoothing Gaussian Filter 62
Filters. Materials from Prof. Klaus Mueller
Filters Materials from Prof. Klaus Mueller Think More about Pixels What exactly a pixel is in an image or on the screen? Solid square? This cannot be implemented A dot? Yes, but size matters Pixel Dots
More informationTransforms and Frequency Filtering
Transforms and Frequency Filtering Khalid Niazi Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading Instructions Chapter 4: Image Enhancement in the Frequency
More informationDIGITAL SIGNAL PROCESSING CCC-INAOE AUTUMN 2015
DIGITAL SIGNAL PROCESSING CCC-INAOE AUTUMN 2015 Fourier Transform Properties Claudia Feregrino-Uribe & Alicia Morales Reyes Original material: Rene Cumplido "The Scientist and Engineer's Guide to Digital
More informationCoE4TN4 Image Processing. Chapter 4 Filtering in the Frequency Domain
CoE4TN4 Image Processing Chapter 4 Filtering in the Frequency Domain Fourier Transform Sections 4.1 to 4.5 will be done on the board 2 2D Fourier Transform 3 2D Sampling and Aliasing 4 2D Sampling and
More informationComputer Graphics (Fall 2011) Outline. CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi
Computer Graphics (Fall 2011) CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi Some slides courtesy Thomas Funkhouser and Pat Hanrahan Adapted version of CS 283 lecture http://inst.eecs.berkeley.edu/~cs283/fa10
More informationThe Discrete Fourier Transform. Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido
The Discrete Fourier Transform Claudia Feregrino-Uribe, Alicia Morales-Reyes Original material: Dr. René Cumplido CCC-INAOE Autumn 2015 The Discrete Fourier Transform Fourier analysis is a family of mathematical
More informationDiscrete Fourier Transform (DFT)
Amplitude Amplitude Discrete Fourier Transform (DFT) DFT transforms the time domain signal samples to the frequency domain components. DFT Signal Spectrum Time Frequency DFT is often used to do frequency
More informationFourier Transform Pairs
CHAPTER Fourier Transform Pairs For every time domain waveform there is a corresponding frequency domain waveform, and vice versa. For example, a rectangular pulse in the time domain coincides with a sinc
More informationLecture 2: SIGNALS. 1 st semester By: Elham Sunbu
Lecture 2: SIGNALS 1 st semester 1439-2017 1 By: Elham Sunbu OUTLINE Signals and the classification of signals Sine wave Time and frequency domains Composite signals Signal bandwidth Digital signal Signal
More informationDigital Image Processing. Lecture # 3 Image Enhancement
Digital Image Processing Lecture # 3 Image Enhancement 1 Image Enhancement Image Enhancement 3 Image Enhancement 4 Image Enhancement Process an image so that the result is more suitable than the original
More informationSubband coring for image noise reduction. Edward H. Adelson Internal Report, RCA David Sarnoff Research Center, Nov
Subband coring for image noise reduction. dward H. Adelson Internal Report, RCA David Sarnoff Research Center, Nov. 26 1986. Let an image consisting of the array of pixels, (x,y), be denoted (the boldface
More informationProf. Vidya Manian Dept. of Electrical and Comptuer Engineering
Image Processing Intensity Transformations Chapter 3 Prof. Vidya Manian Dept. of Electrical and Comptuer Engineering INEL 5327 ECE, UPRM Intensity Transformations 1 Overview Background Basic intensity
More information06: Thinking in Frequencies. CS 5840: Computer Vision Instructor: Jonathan Ventura
06: Thinking in Frequencies CS 5840: Computer Vision Instructor: Jonathan Ventura Decomposition of Functions Taylor series: Sum of polynomials f(x) =f(a)+f 0 (a)(x a)+ f 00 (a) 2! (x a) 2 + f 000 (a) (x
More information1.Discuss the frequency domain techniques of image enhancement in detail.
1.Discuss the frequency domain techniques of image enhancement in detail. Enhancement In Frequency Domain: The frequency domain methods of image enhancement are based on convolution theorem. This is represented
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Filter Design Circularly symmetric 2-D low-pass filter Pass-band radial frequency: ω p Stop-band radial frequency: ω s 1 δ p Pass-band tolerances: δ
More informationDigital Image Processing
Digital Image Processing Part 2: Image Enhancement Digital Image Processing Course Introduction in the Spatial Domain Lecture AASS Learning Systems Lab, Teknik Room T26 achim.lilienthal@tech.oru.se Course
More informationProf. Feng Liu. Fall /04/2018
Prof. Feng Liu Fall 2018 http://www.cs.pdx.edu/~fliu/courses/cs447/ 10/04/2018 1 Last Time Image file formats Color quantization 2 Today Dithering Signal Processing Homework 1 due today in class Homework
More informationImage Enhancement in the Spatial Domain (Part 1)
Image Enhancement in the Spatial Domain (Part 1) Lecturer: Dr. Hossam Hassan Email : hossameldin.hassan@eng.asu.edu.eg Computers and Systems Engineering Principle Objective of Enhancement Process an image
More informationSECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS
RADT 3463 - COMPUTERIZED IMAGING Section I: Chapter 2 RADT 3463 Computerized Imaging 1 SECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS RADT 3463 COMPUTERIZED IMAGING Section I: Chapter 2 RADT
More informationSAMPLING THEORY. Representing continuous signals with discrete numbers
SAMPLING THEORY Representing continuous signals with discrete numbers Roger B. Dannenberg Professor of Computer Science, Art, and Music Carnegie Mellon University ICM Week 3 Copyright 2002-2013 by Roger
More informationIMAGE ENHANCEMENT IN SPATIAL DOMAIN
A First Course in Machine Vision IMAGE ENHANCEMENT IN SPATIAL DOMAIN By: Ehsan Khoramshahi Definitions The principal objective of enhancement is to process an image so that the result is more suitable
More informationImage Enhancement in Spatial Domain
Image Enhancement in Spatial Domain 2 Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application scenarios
More informationDISCRETE FOURIER TRANSFORM AND FILTER DESIGN
DISCRETE FOURIER TRANSFORM AND FILTER DESIGN N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 03 Spectrum of a Square Wave 2 Results of Some Filters 3 Notation 4 x[n]
More informationSampling and Reconstruction
Sampling and Reconstruction Peter Rautek, Eduard Gröller, Thomas Theußl Institute of Computer Graphics and Algorithms Vienna University of Technology Motivation Theory and practice of sampling and reconstruction
More informationLecture 3 Complex Exponential Signals
Lecture 3 Complex Exponential Signals Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/1 1 Review of Complex Numbers Using Euler s famous formula for the complex exponential The
More information2D Discrete Fourier Transform
2D Discrete Fourier Transform In these lecture notes the figures have been removed for copyright reasons. References to figures are given instead, please check the figures yourself as given in the course
More information8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and
8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE
More informationTopic 6. The Digital Fourier Transform. (Based, in part, on The Scientist and Engineer's Guide to Digital Signal Processing by Steven Smith)
Topic 6 The Digital Fourier Transform (Based, in part, on The Scientist and Engineer's Guide to Digital Signal Processing by Steven Smith) 10 20 30 40 50 60 70 80 90 100 0-1 -0.8-0.6-0.4-0.2 0 0.2 0.4
More informationUSE OF FT IN IMAGE PROCESSING IMAGE PROCESSING (RRY025)
IMAGE PROCESSIG (RRY25) USE OF FT I IMAGE PROCESSIG Optics- originofimperfectionsinimagingsystems(limited resolution/blurring related to 2D FTs)- need to understand using Continuous FT. Sampling -Capturecontinuousimageontoasetofdiscrete
More informationFourier transforms, SIM
Fourier transforms, SIM Last class More STED Minflux Fourier transforms This class More FTs 2D FTs SIM 1 Intensity.5 -.5 FT -1.5 1 1.5 2 2.5 3 3.5 4 4.5 5 6 Time (s) IFT 4 2 5 1 15 Frequency (Hz) ff tt
More informationDigital Image Processing
Digital Image Processing Lecture # 5 Image Enhancement in Spatial Domain- I ALI JAVED Lecturer SOFTWARE ENGINEERING DEPARTMENT U.E.T TAXILA Email:: ali.javed@uettaxila.edu.pk Office Room #:: 7 Presentation
More informationChapter 3, Part 4: Intro to the Trigonometric Functions
Haberman MTH Section I: The Trigonometric Functions Chapter, Part : Intro to the Trigonometric Functions Recall that the sine and cosine function represent the coordinates of points in the circumference
More informationקורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
קורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור What is an image? An image is a discrete array of samples representing a continuous
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 15 Image Processing 14/04/15 http://www.ee.unlv.edu/~b1morris/ee482/
More informationImage Processing Lecture 4
Image Enhancement Image enhancement aims to process an image so that the output image is more suitable than the original. It is used to solve some computer imaging problems, or to improve image quality.
More informationUnderstanding Digital Signal Processing
Understanding Digital Signal Processing Richard G. Lyons PRENTICE HALL PTR PRENTICE HALL Professional Technical Reference Upper Saddle River, New Jersey 07458 www.photr,com Contents Preface xi 1 DISCRETE
More informationImage Processing. What is an image? קורס גרפיקה ממוחשבת 2008 סמסטר ב' Converting to digital form. Sampling and Reconstruction.
Amplitude 5/1/008 What is an image? An image is a discrete array of samples representing a continuous D function קורס גרפיקה ממוחשבת 008 סמסטר ב' Continuous function Discrete samples 1 חלק מהשקפים מעובדים
More information21/01/2014. Fundamentals of the analysis of neuronal oscillations. Separating sources
21/1/214 Separating sources Fundamentals of the analysis of neuronal oscillations Robert Oostenveld Donders Institute for Brain, Cognition and Behaviour Radboud University Nijmegen, The Netherlands Use
More informationMidterm Review. Image Processing CSE 166 Lecture 10
Midterm Review Image Processing CSE 166 Lecture 10 Topics covered Image acquisition, geometric transformations, and image interpolation Intensity transformations Spatial filtering Fourier transform and
More informationCS6670: Computer Vision Noah Snavely. Administrivia. Administrivia. Reading. Last time: Convolution. Last time: Cross correlation 9/8/2009
CS667: Computer Vision Noah Snavely Administrivia New room starting Thursday: HLS B Lecture 2: Edge detection and resampling From Sandlot Science Administrivia Assignment (feature detection and matching)
More information!"!#"#$% Lecture 2: Media Creation. Some materials taken from Prof. Yao Wang s slides RECAP
Lecture 2: Media Creation Some materials taken from Prof. Yao Wang s slides RECAP #% A Big Umbrella Content Creation: produce the media, compress it to a format that is portable/ deliverable Distribution:
More informationMidterm is on Thursday!
Midterm is on Thursday! Project presentations are May 17th, 22nd and 24th Next week there is a strike on campus. Class is therefore cancelled on Tuesday. Please work on your presentations instead! REVIEW
More informationSPATIAL VISION. ICS 280: Visual Perception. ICS 280: Visual Perception. Spatial Frequency Theory. Spatial Frequency Theory
SPATIAL VISION Spatial Frequency Theory So far, we have considered, feature detection theory Recent development Spatial Frequency Theory The fundamental elements are spatial frequency elements Does not
More informationImage features: Histograms, Aliasing, Filters, Orientation and HOG. D.A. Forsyth
Image features: Histograms, Aliasing, Filters, Orientation and HOG D.A. Forsyth Simple color features Histogram of image colors in a window Opponent color representations R-G B-Y=B-(R+G)/2 Intensity=(R+G+B)/3
More informationTRANSFORMS / WAVELETS
RANSFORMS / WAVELES ransform Analysis Signal processing using a transform analysis for calculations is a technique used to simplify or accelerate problem solution. For example, instead of dividing two
More informationImage Filtering and Gaussian Pyramids
Image Filtering and Gaussian Pyramids CS94: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 27 Limitations of Point Processing Q: What happens if I reshuffle all pixels within
More informationTopic 2. Signal Processing Review. (Some slides are adapted from Bryan Pardo s course slides on Machine Perception of Music)
Topic 2 Signal Processing Review (Some slides are adapted from Bryan Pardo s course slides on Machine Perception of Music) Recording Sound Mechanical Vibration Pressure Waves Motion->Voltage Transducer
More informationSampling and Reconstruction
Sampling and Reconstruction Many slides from Steve Marschner 15-463: Computational Photography Alexei Efros, CMU, Fall 211 Sampling and Reconstruction Sampled representations How to store and compute with
More informationFiltering Images in the Spatial Domain Chapter 3b G&W. Ross Whitaker (modified by Guido Gerig) School of Computing University of Utah
Filtering Images in the Spatial Domain Chapter 3b G&W Ross Whitaker (modified by Guido Gerig) School of Computing University of Utah 1 Overview Correlation and convolution Linear filtering Smoothing, kernels,
More informationBiomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar
Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative
More informationAlgebra and Trig. I. The graph of
Algebra and Trig. I 4.5 Graphs of Sine and Cosine Functions The graph of The graph of. The trigonometric functions can be graphed in a rectangular coordinate system by plotting points whose coordinates
More informationImage Processing Final Test
Image Processing 048860 Final Test Time: 100 minutes. Allowed materials: A calculator and any written/printed materials are allowed. Answer 4-6 complete questions of the following 10 questions in order
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationTDI2131 Digital Image Processing
TDI2131 Digital Image Processing Image Enhancement in Spatial Domain Lecture 3 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs.
More informationLast Lecture. photomatix.com
Last Lecture photomatix.com Today Image Processing: from basic concepts to latest techniques Filtering Edge detection Re-sampling and aliasing Image Pyramids (Gaussian and Laplacian) Removing handshake
More informationWhat is image enhancement? Point operation
IMAGE ENHANCEMENT 1 What is image enhancement? Image enhancement techniques Point operation 2 What is Image Enhancement? Image enhancement is to process an image so that the result is more suitable than
More informationAliasing and Antialiasing. What is Aliasing? What is Aliasing? What is Aliasing?
What is Aliasing? Errors and Artifacts arising during rendering, due to the conversion from a continuously defined illumination field to a discrete raster grid of pixels 1 2 What is Aliasing? What is Aliasing?
More informationAnna University, Chennai B.E./B.TECH DEGREE EXAMINATION, MAY/JUNE 2013 Seventh Semester
www.vidyarthiplus.com Anna University, Chennai B.E./B.TECH DEGREE EXAMINATION, MAY/JUNE 2013 Seventh Semester Electronics and Communication Engineering EC 2029 / EC 708 DIGITAL IMAGE PROCESSING (Regulation
More informationCoE4TN4 Image Processing. Chapter 3: Intensity Transformation and Spatial Filtering
CoE4TN4 Image Processing Chapter 3: Intensity Transformation and Spatial Filtering Image Enhancement Enhancement techniques: to process an image so that the result is more suitable than the original image
More informationSampling and pixels. CS 178, Spring Marc Levoy Computer Science Department Stanford University. Begun 4/23, finished 4/25.
Sampling and pixels CS 178, Spring 2013 Begun 4/23, finished 4/25. Marc Levoy Computer Science Department Stanford University Why study sampling theory? Why do I sometimes get moiré artifacts in my images?
More informationDesign of Various Image Enhancement Techniques - A Critical Review
Design of Various Image Enhancement Techniques - A Critical Review Moole Sasidhar M.Tech Department of Electronics and Communication Engineering, Global College of Engineering and Technology(GCET), Kadapa,
More informationImage Processing for feature extraction
Image Processing for feature extraction 1 Outline Rationale for image pre-processing Gray-scale transformations Geometric transformations Local preprocessing Reading: Sonka et al 5.1, 5.2, 5.3 2 Image
More informationIntroduction to Wavelet Transform. Chapter 7 Instructor: Hossein Pourghassem
Introduction to Wavelet Transform Chapter 7 Instructor: Hossein Pourghassem Introduction Most of the signals in practice, are TIME-DOMAIN signals in their raw format. It means that measured signal is a
More informationNotes on Fourier transforms
Fourier Transforms 1 Notes on Fourier transforms The Fourier transform is something we all toss around like we understand it, but it is often discussed in an offhand way that leads to confusion for those
More informationCS4495/6495 Introduction to Computer Vision. 2C-L3 Aliasing
CS4495/6495 Introduction to Computer Vision 2C-L3 Aliasing Recall: Fourier Pairs (from Szeliski) Fourier Transform Sampling Pairs FT of an impulse train is an impulse train Sampling and Aliasing Sampling
More informationSection 7.1 Graphs of Sine and Cosine
Section 7.1 Graphs of Sine and Cosine OBJECTIVE 1: Understanding the Graph of the Sine Function and its Properties In Chapter 7, we will use a rectangular coordinate system for a different purpose. We
More informationImage acquisition. Midterm Review. Digitization, line of image. Digitization, whole image. Geometric transformations. Interpolation 10/26/2016
Image acquisition Midterm Review Image Processing CSE 166 Lecture 10 2 Digitization, line of image Digitization, whole image 3 4 Geometric transformations Interpolation CSE 166 Transpose these matrices
More informationIMAGE PROCESSING: AREA OPERATIONS (FILTERING)
IMAGE PROCESSING: AREA OPERATIONS (FILTERING) N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 13 IMAGE PROCESSING: AREA OPERATIONS (FILTERING) N. C. State University
More informationMATLAB 6.5 Image Processing Toolbox Tutorial
MATLAB 6.5 Image Processing Toolbox Tutorial The purpose of this tutorial is to gain familiarity with MATLAB s Image Processing Toolbox. This tutorial does not contain all of the functions available in
More informationVision Review: Image Processing. Course web page:
Vision Review: Image Processing Course web page: www.cis.udel.edu/~cer/arv September 7, Announcements Homework and paper presentation guidelines are up on web page Readings for next Tuesday: Chapters 6,.,
More informationFinal Exam Solutions June 7, 2004
Name: Final Exam Solutions June 7, 24 ECE 223: Signals & Systems II Dr. McNames Write your name above. Keep your exam flat during the entire exam period. If you have to leave the exam temporarily, close
More informationWAVELETS: BEYOND COMPARISON - D. L. FUGAL
WAVELETS: BEYOND COMPARISON - D. L. FUGAL Wavelets are used extensively in Signal and Image Processing, Medicine, Finance, Radar, Sonar, Geology and many other varied fields. They are usually presented
More informationImage Processing. Image Processing. What is an Image? Image Resolution. Overview. Sources of Error. Filtering Blur Detect edges
Thomas Funkhouser Princeton University COS 46, Spring 004 Quantization Random dither Ordered dither Floyd-Steinberg dither Pixel operations Add random noise Add luminance Add contrast Add saturation ing
More informationSampling Theory. CS5625 Lecture Steve Marschner. Cornell CS5625 Spring 2016 Lecture 7
Sampling Theory CS5625 Lecture 7 Sampling example (reminder) When we sample a high-frequency signal we don t get what we expect result looks like a lower frequency not possible to distinguish between this
More information8. Lecture. Image restoration: Fourier domain
8. Lecture Image restoration: Fourier domain 1 Structured noise 2 Motion blur 3 Filtering in the Fourier domain ² Spatial ltering (average, Gaussian,..) can be done in the Fourier domain (convolution theorem)
More informationLinear Image Processing
CHAPTER Linear Image Processing Linear image processing is based on the same two techniques as conventional DSP: convolution and Fourier analysis. Convolution is the more important of these two, since
More informationFourier and Wavelets
Fourier and Wavelets Why do we need a Transform? Fourier Transform and the short term Fourier (STFT) Heisenberg Uncertainty Principle The continues Wavelet Transform Discrete Wavelet Transform Wavelets
More informationObjectives. Abstract. This PRO Lesson will examine the Fast Fourier Transformation (FFT) as follows:
: FFT Fast Fourier Transform This PRO Lesson details hardware and software setup of the BSL PRO software to examine the Fast Fourier Transform. All data collection and analysis is done via the BIOPAC MP35
More informationFilters. Motivating Example. Tracking a fly, oh my! Moving Weighted Average Filter. General Picture
Motivating Example Filters Consider we are tracking a fly Sensor reports the fly s position several times a second Some noise in the sensor Goal: reconstruct the fly s actual path Problem: can t rely on
More informationDigital Image Processing COSC 6380/4393
Digital Image Processing COSC 638/4393 Lecture 9 Sept 26 th, 217 Pranav Mantini Slides from Dr. Shishir K Shah and Frank (Qingzhong) Liu, S. Narasimhan HISTOGRAM SHAPING We now describe methods for histogram
More informationFFT Convolution. The Overlap-Add Method
CHAPTER 18 FFT Convolution This chapter presents two important DSP techniques, the overlap-add method, and FFT convolution. The overlap-add method is used to break long signals into smaller segments for
More informationFrequency Domain Representation of Signals
Frequency Domain Representation of Signals The Discrete Fourier Transform (DFT) of a sampled time domain waveform x n x 0, x 1,..., x 1 is a set of Fourier Coefficients whose samples are 1 n0 X k X0, X
More informationTable of contents. Vision industrielle 2002/2003. Local and semi-local smoothing. Linear noise filtering: example. Convolution: introduction
Table of contents Vision industrielle 2002/2003 Session - Image Processing Département Génie Productique INSA de Lyon Christian Wolf wolf@rfv.insa-lyon.fr Introduction Motivation, human vision, history,
More informationPerformance Evaluation of Edge Detection Techniques for Square Pixel and Hexagon Pixel images
Performance Evaluation of Edge Detection Techniques for Square Pixel and Hexagon Pixel images Keshav Thakur 1, Er Pooja Gupta 2,Dr.Kuldip Pahwa 3, 1,M.Tech Final Year Student, Deptt. of ECE, MMU Ambala,
More informationSYLLABUS CHAPTER - 2 : INTENSITY TRANSFORMATIONS. Some Basic Intensity Transformation Functions, Histogram Processing.
Contents i SYLLABUS UNIT - I CHAPTER - 1 : INTRODUCTION TO DIGITAL IMAGE PROCESSING Introduction, Origins of Digital Image Processing, Applications of Digital Image Processing, Fundamental Steps, Components,
More informationCSC 320 H1S CSC320 Exam Study Guide (Last updated: April 2, 2015) Winter 2015
Question 1. Suppose you have an image I that contains an image of a left eye (the image is detailed enough that it makes a difference that it s the left eye). Write pseudocode to find other left eyes in
More informationFrequency Domain Enhancement
Tutorial Report Frequency Domain Enhancement Page 1 of 21 Frequency Domain Enhancement ESE 558 - DIGITAL IMAGE PROCESSING Tutorial Report Instructor: Murali Subbarao Written by: Tutorial Report Frequency
More informationQäf) Newnes f-s^j^s. Digital Signal Processing. A Practical Guide for Engineers and Scientists. by Steven W. Smith
Digital Signal Processing A Practical Guide for Engineers and Scientists by Steven W. Smith Qäf) Newnes f-s^j^s / *" ^"P"'" of Elsevier Amsterdam Boston Heidelberg London New York Oxford Paris San Diego
More informationECC419 IMAGE PROCESSING
ECC419 IMAGE PROCESSING INTRODUCTION Image Processing Image processing is a subclass of signal processing concerned specifically with pictures. Digital Image Processing, process digital images by means
More informationPostprocessing of nonuniform MRI
Postprocessing of nonuniform MRI Wolfgang Stefan, Anne Gelb and Rosemary Renaut Arizona State University Oct 11, 2007 Stefan, Gelb, Renaut (ASU) Postprocessing October 2007 1 / 24 Outline 1 Introduction
More informationSignal Processing. Naureen Ghani. December 9, 2017
Signal Processing Naureen Ghani December 9, 27 Introduction Signal processing is used to enhance signal components in noisy measurements. It is especially important in analyzing time-series data in neuroscience.
More informationPart 2: Fourier transforms. Key to understanding NMR, X-ray crystallography, and all forms of microscopy
Part 2: Fourier transforms Key to understanding NMR, X-ray crystallography, and all forms of microscopy Sine waves y(t) = A sin(wt + p) y(x) = A sin(kx + p) To completely specify a sine wave, you need
More informationPrecalculus Lesson 9.2 Graphs of Polar Equations Mrs. Snow, Instructor
Precalculus Lesson 9.2 Graphs of Polar Equations Mrs. Snow, Instructor As we studied last section points may be described in polar form or rectangular form. Likewise an equation may be written using either
More informationLast Lecture. photomatix.com
Last Lecture photomatix.com HDR Video Assorted pixel (Single Exposure HDR) Assorted pixel Assorted pixel Pixel with Adaptive Exposure Control light attenuator element detector element T t+1 I t controller
More informationWavelets and wavelet convolution and brain music. Dr. Frederike Petzschner Translational Neuromodeling Unit
Wavelets and wavelet convolution and brain music Dr. Frederike Petzschner Translational Neuromodeling Unit 06.03.2015 Recap Why are we doing this? We know that EEG data contain oscillations. Or goal is
More informationDigital Signal Processing for Audio Applications
Digital Signal Processing for Audio Applications Volime 1 - Formulae Third Edition Anton Kamenov Digital Signal Processing for Audio Applications Third Edition Volume 1 Formulae Anton Kamenov 2011 Anton
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Multi-Resolution Processing Gaussian Pyramid Starting with an image x[n], which we will also label x 0 [n], Construct a sequence of progressively lower
More informationOn spatial resolution
On spatial resolution Introduction How is spatial resolution defined? There are two main approaches in defining local spatial resolution. One method follows distinction criteria of pointlike objects (i.e.
More informationDesign of FIR Filters
Design of FIR Filters Elena Punskaya www-sigproc.eng.cam.ac.uk/~op205 Some material adapted from courses by Prof. Simon Godsill, Dr. Arnaud Doucet, Dr. Malcolm Macleod and Prof. Peter Rayner 1 FIR as a
More informationNon Linear Image Enhancement
Non Linear Image Enhancement SAIYAM TAKKAR Jaypee University of information technology, 2013 SIMANDEEP SINGH Jaypee University of information technology, 2013 Abstract An image enhancement algorithm based
More information