1-D Sampling. Letf s = 1/T be the sampling frequency. What is the relationship between S(e jω ) and G(f)? S(e jω ) = 1. Intuition Scale frequencies
|
|
- Buck Lambert
- 5 years ago
- Views:
Transcription
1 C. A. Bouman: Digital Image Processing - January 12, D Sampling g(t) Continuous timeinput Sampler PeriodT s(n) = g(nt) Discrete timeoutput Letf s = 1/T be the sampling frequency. What is the relationship between S(e jω ) and G(f)? S(e jω ) = 1 ( ) ω 2πk G T 2πT Intuition Scale frequencies Replicate at period 2π Apply gain factor of 1 T. k= f = 0 ω = 0 f = 1 2T = 1 2 f s ω = π f = 1 T = f s ω = 2π
2 .. C. A. Bouman: Digital Image Processing - January 12, D Sampling 1 G(f) 1 2 f s f s 1 T S(e jω ) 4π 3π 2π π 0 π 2π 3π 4π Scale frequencies Replicate at period 2π Apply gain factor of 1 T. CT Frequency DT Frequency f s π f s 2π
3 C. A. Bouman: Digital Image Processing - January 12, D Sampling g(x,y) Continuous space input Sampler period ( T, T ) x y s(m,n) = g(mt x,nt y) Discrete space output Let T x and T y be the sampling period in the x and y dimensions. Then S(e jµ,e jν ) = 1 T x T y Intuition Scale frequencies k= l= G ( µ 2πk, ν 2πl ) 2πT x 2πT y (u,v) = (0,0) (µ,ν) = (0,0) (u,v) = ( 1 2T x,0) (µ,ν) = (π,0) (u,v) = (0, (u,v) = ( 1 2T x, 1 2T y ) (µ,ν) = (0,π) 1 2T y ) (µ,ν) = (π,π)
4 C. A. Bouman: Digital Image Processing - January 12, Replicate along both µ and ν with period 2π Apply gain factor of 1 T x T y.
5 C. A. Bouman: Digital Image Processing - January 12, Example 1: 2-D Sampling Without Aliasing G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image. v u
6 C. A. Bouman: Digital Image Processing - January 12, Example 2: 2-D Sampling With Aliasing G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image.
7 C. A. Bouman: Digital Image Processing - January 12, Nyquist Condition A continuous-space signal, g(x, y), may be uniquely reconstructed from its sampled version,s(m,n), ifg(u,v) = 0 for all u > 1 2T x and v > 1 2T y. This condition is sufficient, but not necessary.
8 C. A. Bouman: Digital Image Processing - January 12, Example 3: Nonrectangular Spectral Support G(u,v) - Spectrum of continuous space image. v 1/(2 T y ) 1/(2T ) x u S(e jµ,e jν ) - Spectrum of sampled image.
9 C. A. Bouman: Digital Image Processing - January 12, Focal Plain Arrays Typical Charged Coupled Devices (CCD) Imaging array A CCD Cell T T Solid state device used in video and still cameras. Each cell collects photons in a squaret T region. Response of each cell is linear with energy (photons). Signal is shifted out after capture. Cell should be large for best sensitivity. Finite cell size violates sampling assumptions.
10 C. A. Bouman: Digital Image Processing - January 12, Mathematical Model for CCD Lets(m,n) be the output of cell (m,n), then s(m,n) = h(x mt,y nt)g(x,y)dxdy IR 2 whereh(x,y) is the rectangular window for each cell. h(x,y) = 1 T 2rect(x/T,y/T) Define g(x,y) so that g(ξ,η) = h(x ξ,y η)g(x,y)dxdy IR 2 = h( x, y) g(x,y) and then we have that s(m,n) = g(mt,nt)
11 C. A. Bouman: Digital Image Processing - January 12, CCD Model in Space Domain Filter signal with space reversed cell profile g(x,y) = h( x, y) g(x,y) Sample filtered image = 1 T 2rect(x/T,y/T) g(x,y) s(m,n) = g(mt,nt) Cell aperture blurs image.
12 C. A. Bouman: Digital Image Processing - January 12, CCD Model in Frequency Domain Filter signal with cell profile G(u,v) = H (u,v)g(u,v) = sinc(ut, vt)g(u, v) Sample filtered image S(e jµ,e jν ) = 1 T 2 k= l= ( ) µ 2πk 2πl G,ν 2πT 2πT Complete model S(e jµ,e jν ) = 1 T 2 k= l= Sinc function filters image. ( µ 2πk sinc 2π, ν 2πl ) 2π ( ) µ 2πk 2πl G,ν 2πT 2πT
13 C. A. Bouman: Digital Image Processing - January 12, Sampled Image Display or Rendering (Reconstruction) CRT s and LCD displays convert discrete-space images to continuous-space images. Notation: Model: s(m,n) - sampled image p(x,y) - point spread function (PSF) of display f(x, y) - displayed image In space domain: f(x,y) = m= n= In frequency domain: s(m,n)p(x mt,y nt) F(u,v) = P(u,v)S(e j2πtu,e j2πtv ) µ 2πTu ν 2πTv Monitor PSF further softens image.
14 C. A. Bouman: Digital Image Processing - January 12, Model for Sampling and Reconstruction Combining models for sampling and reconstruction results in: F(u,v) = P(u,v) T 2 k= l= When no aliasing occurs, this reduces to ( H u K T,v l ) ( G u K T T,v l ) T F(u,v) = P(u,v)H (u,v) G(u,v) T 2 = P(u,v) sinc(ut,vt) G(u,v) T 2
15 C. A. Bouman: Digital Image Processing - January 12, Effect of Sampling and Reconstruction The image is effectively filtered by the transfer function 1 T 2P(u,v)H (u,v) = 1 T 2P(u,v)sinc(uT,vT) Scanned image normally must be sharpened to remove the effect of softening produced in the scanning and display processes.
16 C. A. Bouman: Digital Image Processing - January 12, Raster Scan Ordering Specific scan pattern for mapping 2-D images to 1-D. Order pixels from top to bottom and left to right. Example: Consider the discrete-space image f(m,n) f(0,0) f(m 1,0)..... f(0,n 1) f(m 1,N 1) Raster ordering produces a 1-D signalx n x 0 x 1 x M 1 x M x M+1 x 2 M x (N 1) M x (N 1) M+1 x N M 1
17 C. A. Bouman: Digital Image Processing - January 12, Vector Representation of Images An image is not a matrix. (A Matrix specifies a linear function.) Vectorizing images Often image must be converted to a vector (data). Vector looks like x = f(0,0). f(m 1,0). f(0,n 1). f(m 1,N 1) Mapping from vector to image isf(m,n) = x n M+m.
The main goal of Computer Graphics is to generate 2D images 2D images are continuous 2D functions (or signals)
Motivation The main goal of Computer Graphics is to generate 2D images 2D images are continuous 2D functions (or signals) monochrome f(x,y) or color r(x,y), g(x,y), b(x,y) These functions are represented
More informationMotivation. The main goal of Computer Graphics is to generate 2D images. 2D images are continuous 2D functions (or signals)
Motivation The main goal of Computer Graphics is to generate 2D images 2D images are continuous 2D functions (or signals) monochrome f(x,y) or color r(x,y), g(x,y), b(x,y) These functions are represented
More informationImage Restoration. Yao Wang Polytechnic Institute of NYU, Brooklyn, NY 11201
Image Restoration Yao Wang Polytechnic Institute of NYU, Brooklyn, NY 11201 Partly based on A. K. Jain, Fundamentals of Digital Image Processing, and Gonzalez/Woods, Digital Image Processing Figures from
More informationReading. 2. Fourier analysis and sampling theory. Required: Watt, Section 14.1 Recommended:
Reading Required: Watt, Section 14.1 Recommended: 2. Fourier analysis and sampling theory Ron Bracewell, The Fourier Transform and Its Applications, McGraw-Hill. Don P. Mitchell and Arun N. Netravali,
More informationLecture 2: 2D Fourier transforms and applications
Lecture 2: 2D Fourier transforms and applications B14 Image Analysis Michaelmas 2017 Dr. M. Fallon Fourier transforms and spatial frequencies in 2D Definition and meaning The Convolution Theorem Applications
More informationDigital Image Processing. Lecture 6
Digital Image Processing Lecture 6 (Enhancement in the Frequency domain) Bu-Ali Sina University Computer Engineering Dep. Fall 2016 Image Enhancement In The Frequency Domain Outline Jean Baptiste Joseph
More informationFourier analysis and sampling theory
Reading Required: Shirley, Ch. 9 Recommended: Fourier analysis and sampling theory Ron Bracewell, The Fourier Transform and Its Applications, McGraw-Hill. Don P. Mitchell and Arun N. Netravali, Reconstruction
More informationImage warping introduction
Image warping introduction 1997-2015 Josef Pelikán CGG MFF UK Praha pepca@cgg.mff.cuni.cz http://cgg.mff.cuni.cz/~pepca/ Warping 2015 Josef Pelikán, http://cgg.mff.cuni.cz/~pepca 1 / 22 Warping.. image
More informationSampling and Reconstruction
Page 1 Sampling and Reconstruction The sampling and reconstruction process Real world: continuous Digital world: discrete Basic signal processing Fourier transforms The convolution theorem The sampling
More informationComputer Graphics. Texture Filtering & Sampling Theory. Hendrik Lensch. Computer Graphics WS07/08 Texturing
Computer Graphics Texture Filtering & Sampling Theory Hendrik Lensch Overview Last time Texture Parameterization Procedural Shading Today Texturing Filtering 2D Texture Mapping Forward mapping Object surface
More informationContinuous Space Fourier Transform (CSFT)
C. A. Bouman: Digital Image Processing - January 8, 8 Continuous Space Fourier Transform (CSFT) Forward CSFT: F(u,v) = Inverse CSFT: f(x,y) = f(x,y)e jπ(ux+vy) dxdy F(u,v)e jπ(ux+vy) dudv Space coordinates:.
More informationSampling: Application to 2D Transformations
Sampling: Application to 2D Transformations University of the Philippines - Diliman August Diane Lingrand lingrand@polytech.unice.fr http://www.essi.fr/~lingrand Sampling Computer images are manipulated
More informationCoE4TN4 Image Processing. Chapter 5 Image Restoration and Reconstruction
CoE4TN4 Image Processing Chapter 5 Image Restoration and Reconstruction Image Restoration Similar to image enhancement, the ultimate goal of restoration techniques is to improve an image Restoration: a
More informationComputer Vision and Graphics (ee2031) Digital Image Processing I
Computer Vision and Graphics (ee203) Digital Image Processing I Dr John Collomosse J.Collomosse@surrey.ac.uk Centre for Vision, Speech and Signal Processing University of Surrey Learning Outcomes After
More informationLecture 5: Frequency Domain Transformations
#1 Lecture 5: Frequency Domain Transformations Saad J Bedros sbedros@umn.edu From Last Lecture Spatial Domain Transformation Point Processing for Enhancement Area/Mask Processing Transformations Image
More informationEECS490: Digital Image Processing. Lecture #16
Lecture #16 Wiener Filters Constrained Least Squares Filter Computed Tomography Basics Reconstruction and the Radon Transform Fourier Slice Theorem Filtered Backprojections Fan Beams Motion Blurring Model
More informationFilters and Fourier Transforms
Filters and Fourier Transforms NOTE: Before reading these notes, see 15-462 Basic Raster notes: http://www.cs.cmu.edu/afs/cs/academic/class/15462/web/notes/notes.html OUTLINE: The Cost of Filtering Fourier
More informationSYSTEMS OF NONLINEAR EQUATIONS
SYSTEMS OF NONLINEAR EQUATIONS Widely used in the mathematical modeling of real world phenomena. We introduce some numerical methods for their solution. For better intuition, we examine systems of two
More informationPhys. 428, Lecture 6
Phys. 428, Lecture 6 Mid-Term & Weekly questions Since we are behind in the material, there will be no midterm Instead the final project will be subdivided into graded stages Class Project Pick: An imaging
More informationIntroduction to Computer Vision. Introduction CMPSCI 591A/691A CMPSCI 570/670. Image Formation
Introduction CMPSCI 591A/691A CMPSCI 570/670 Image Formation Lecture Outline Light and Optics Pinhole camera model Perspective projection Thin lens model Fundamental equation Distortion: spherical & chromatic
More informationImage Processing Lecture 10
Image Restoration Image restoration attempts to reconstruct or recover an image that has been degraded by a degradation phenomenon. Thus, restoration techniques are oriented toward modeling the degradation
More informationVivekananda. Collegee of Engineering & Technology. Question and Answers on 10CS762 /10IS762 UNIT- 5 : IMAGE ENHANCEMENT.
Vivekananda Collegee of Engineering & Technology Question and Answers on 10CS762 /10IS762 UNIT- 5 : IMAGE ENHANCEMENT Dept. Prepared by Harivinod N Assistant Professor, of Computer Science and Engineering,
More informationComputer Vision. The image formation process
Computer Vision The image formation process Filippo Bergamasco (filippo.bergamasco@unive.it) http://www.dais.unive.it/~bergamasco DAIS, Ca Foscari University of Venice Academic year 2016/2017 The image
More information3. Image formation, Fourier analysis and CTF theory. Paula da Fonseca
3. Image formation, Fourier analysis and CTF theory Paula da Fonseca EM course 2017 - Agenda - Overview of: Introduction to Fourier analysis o o o o Sine waves Fourier transform (simple examples of 1D
More informationBME I5000: Biomedical Imaging
1 Lucas Parra, CCNY BME I5000: Biomedical Imaging Lecture 11 Point Spread Function, Inverse Filtering, Wiener Filtering, Sharpening,... Lucas C. Parra, parra@ccny.cuny.edu Blackboard: http://cityonline.ccny.cuny.edu/
More informationExamination in Digital Image Processing, TSBB08
Examination in Digital Image Processing, TSBB8 Time: 215-1-23, 14.-18. Location: TER4 Examiner: Maria Magnusson will visit the classroom at 15. and 16.45, tel. 281336, 73-84 38 67 Permitted equipment:
More informationLecture 16: Computer Vision
CS4442/9542b: Artificial Intelligence II Prof. Olga Veksler Lecture 16: Computer Vision Motion Slides are from Steve Seitz (UW), David Jacobs (UMD) Outline Motion Estimation Motion Field Optical Flow Field
More informationDefinition: A graph G = (V, E) is called a tree if G is connected and acyclic. The following theorem captures many important facts about trees.
Tree 1. Trees and their Properties. Spanning trees 3. Minimum Spanning Trees 4. Applications of Minimum Spanning Trees 5. Minimum Spanning Tree Algorithms 1.1 Properties of Trees: Definition: A graph G
More informationImage Enhancement in Spatial Domain. By Dr. Rajeev Srivastava
Image Enhancement in Spatial Domain By Dr. Rajeev Srivastava CONTENTS Image Enhancement in Spatial Domain Spatial Domain Methods 1. Point Processing Functions A. Gray Level Transformation functions for
More informationComputer Graphics. Sampling Theory & Anti-Aliasing. Philipp Slusallek
Computer Graphics Sampling Theory & Anti-Aliasing Philipp Slusallek Dirac Comb (1) Constant & δ-function flash Comb/Shah function 2 Dirac Comb (2) Constant & δ-function Duality f(x) = K F(ω) = K (ω) And
More informationA New Approach for the Mathematical Modeling of the GPS/INS Supported Phototriangulation using Kalman Filtering Process
A New Approach for the Mathematical Modeling of the GPS/INS Supported Phototriangulation using Kalman Filtering Process A. Azizi, M.A. Sharifi and J. Amiri Parian Surveying and Geomatics Engineering Faculty
More informationCOMP 9517 Computer Vision
COMP 9517 Computer Vision Frequency Domain Techniques 1 Frequency Versus Spa
More informationThe Divergence Theorem
The Divergence Theorem MATH 311, Calculus III J. Robert Buchanan Department of Mathematics Summer 2011 Green s Theorem Revisited Green s Theorem: M(x, y) dx + N(x, y) dy = C R ( N x M ) da y y x Green
More informationComputer Vision: 4. Filtering. By I-Chen Lin Dept. of CS, National Chiao Tung University
Computer Vision: 4. Filtering By I-Chen Lin Dept. of CS, National Chiao Tung University Outline Impulse response and convolution. Linear filter and image pyramid. Textbook: David A. Forsyth and Jean Ponce,
More informationChapter6 Image Reconstruction
Chapter6 Image Reconstruction Preview 61I 6.1 Introduction 6.2 Reconstruction by Fourier Inversion 6.3 Reconstruction by convolution and backprojection 6.4 Finite series-expansion 1 Preview Reconstruction
More informationf(x,y) is the original image H is the degradation process (or function) n(x,y) represents noise g(x,y) is the obtained degraded image p q
Image Restoration Image Restoration G&W Chapter 5 5.1 The Degradation Model 5.2 5.105.10 browse through the contents 5.11 Geometric Transformations Goal: Reconstruct an image that has been degraded in
More informationChapter 3: Intensity Transformations and Spatial Filtering
Chapter 3: Intensity Transformations and Spatial Filtering 3.1 Background 3.2 Some basic intensity transformation functions 3.3 Histogram processing 3.4 Fundamentals of spatial filtering 3.5 Smoothing
More informationUlrik Söderström 17 Jan Image Processing. Introduction
Ulrik Söderström ulrik.soderstrom@tfe.umu.se 17 Jan 2017 Image Processing Introduction Image Processsing Typical goals: Improve images for human interpretation Image processing Processing of images for
More informationLecture 16: Computer Vision
CS442/542b: Artificial ntelligence Prof. Olga Veksler Lecture 16: Computer Vision Motion Slides are from Steve Seitz (UW), David Jacobs (UMD) Outline Motion Estimation Motion Field Optical Flow Field Methods
More informationCS4670: Computer Vision
CS4670: Computer Vision Noah Snavely Lecture 9: Image alignment http://www.wired.com/gadgetlab/2010/07/camera-software-lets-you-see-into-the-past/ Szeliski: Chapter 6.1 Reading All 2D Linear Transformations
More informationCentral Slice Theorem
Central Slice Theorem Incident X-rays y f(x,y) R x r x Detected p(, x ) The thick line is described by xcos +ysin =R Properties of Fourier Transform F [ f ( x a)] F [ f ( x)] e j 2 a Spatial Domain Spatial
More informationImage processing in frequency Domain
Image processing in frequency Domain Introduction to Frequency Domain Deal with images in: -Spatial domain -Frequency domain Frequency Domain In the frequency or Fourier domain, the value and location
More informationReview. Stephen J. Guy
Review Stephen J. Guy Overview Pixar short Review last class Review course Area of Graphics Image Processing Rendering Modeling Animation Misc Area of Graphics Image Processing Rendering Modeling Animation
More information2D Image Processing INFORMATIK. Kaiserlautern University. DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
2D Image Processing - Filtering Prof. Didier Stricker Kaiserlautern University http://ags.cs.uni-kl.de/ DFKI Deutsches Forschungszentrum für Künstliche Intelligenz http://av.dfki.de 1 What is image filtering?
More informationMagnetic Resonance Imaging (MRI)
C. A. Bouman: Digital Image Processing - January 12, 215 1 Magnetic Resonance Imaging (MRI) Can be very high resolution No radiation exposure Very flexible and programable Tends to be expensive, noisy,
More informationEE368/CS232 Digital Image Processing Winter Homework #4 Solutions
. Moire Pattern Suppression in Radiographs Part A: EE368/CS232 Digital Image Processing Winter 27-28 Homework #4 Solutions Original Image Median Filtered Image (7x7 window) Original Image Median Filtered
More informationSampling Theory in 1D. Point Lattices in Sampling Theory Multidimensional Sampling Theory. Sampling in 1D. Reconstruction in 1D
Sampling heory in 1D Point Lattices in Sampling heory Multidimensional Sampling heory Alireza Entezari aentezar@cs.sfu.ca Computing Science, Simon Fraser University Continuous-domain function Fourier domain
More informationPoint and Spatial Processing
Filtering 1 Point and Spatial Processing Spatial Domain g(x,y) = T[ f(x,y) ] f(x,y) input image g(x,y) output image T is an operator on f Defined over some neighborhood of (x,y) can operate on a set of
More informationDrawing a Triangle (and an Intro to Sampling)
Lecture 4: Drawing a Triangle (and an Intro to Sampling) Computer Graphics CMU 15-462/15-662, Spring 2018 HW 0.5 Due, HW 1 Out Today! GOAL: Implement a basic rasterizer - (Topic of today s lecture) - We
More informationComputed Tomography (Part 2)
EL-GY 6813 / BE-GY 603 / G16.446 Medical Imaging Computed Tomography (Part ) Yao Wang Polytechnic School of Engineering New York University, Brooklyn, NY 1101 Based on Prince and Links, Medical Imaging
More informationAssignment 2. Due Feb 3, 2012
EE225E/BIOE265 Spring 2012 Principles of MRI Miki Lustig Assignment 2 Due Feb 3, 2012 1. Read Nishimura Ch. 3 2. Non-Uniform Sampling. A student has an assignment to monitor the level of Hetch-Hetchi reservoir
More informationCopyright 2005 Center for Imaging Science Rochester Institute of Technology Rochester, NY
Development of Algorithm for Fusion of Hyperspectral and Multispectral Imagery with the Objective of Improving Spatial Resolution While Retaining Spectral Data Thesis Christopher J. Bayer Dr. Carl Salvaggio
More informationUNIT III : IMAGE RESTORATION
UNIT III : IMAGE RESTORATION 1. What is meant by Image Restoration? [AUC NOV/DEC 2013] Restoration attempts to reconstruct or recover an image that has been degraded by using a clear knowledge of the degrading
More informationELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Dynamic Range and Weber s Law HVS is capable of operating over an enormous dynamic range, However, sensitivity is far from uniform over this range Example:
More informationImage Processing: Discrete Images
7 Image Processing: Discrete Images In the previous chapter we explored linear, shift-invariant systems in the continuous two-dimensional domain. In practice, we deal with images that are both limited
More informationAnti-aliasing. Images and Aliasing
CS 130 Anti-aliasing Images and Aliasing Aliasing errors caused by rasterizing How to correct them, in general 2 1 Aliased Lines Stair stepping and jaggies 3 Remove the jaggies Anti-aliased Lines 4 2 Aliasing
More informationProblem 1 (10 Points)
Problem 1 (10 Points) Please explain the following phenomena. The three images shown are the blurred versions of image (a) (Note: vertical bars have width of 5 and height of 100, they are spaced 20 pixels
More informationEEM 561 Machine Vision. Week 3: Fourier Transform and Image Pyramids
EEM 561 Machine Vision Week 3: Fourier Transform and Image Pyramids Spring 2015 Instructor: Hatice Çınar Akakın, Ph.D. haticecinarakakin@anadolu.edu.tr Anadolu University Linear Image Transformations In
More informationImage Processing using LabVIEW. By, Sandip Nair sandipnair.hpage.com
Image Processing using LabVIEW By, Sandip Nair sandipnair06@yahoomail.com sandipnair.hpage.com What is image? An image is two dimensional function, f(x,y), where x and y are spatial coordinates, and the
More informationBeing edited by Prof. Sumana Gupta 1
Being edited by Prof. Sumana Gupta 1 Introduction Digital Image Processing: refers to processing of 2-D picture by a digital Computer- in a broader context refers to digital proc of any 2-d data. A digital
More informationCS 428: Fall Introduction to. Texture mapping and filtering. Andrew Nealen, Rutgers, /18/2010 1
CS 428: Fall 2010 Introduction to Computer Graphics Texture mapping and filtering 10/18/2010 1 Topic overview Image formation and OpenGL Transformations and viewing Polygons and polygon meshes 3D model/mesh
More informationOutline. Sampling and Reconstruction. Sampling and Reconstruction. Foundations of Computer Graphics (Fall 2012)
Foundations of Computer Graphics (Fall 2012) CS 184, Lectures 19: Sampling and Reconstruction http://inst.eecs.berkeley.edu/~cs184 Outline Basic ideas of sampling, reconstruction, aliasing Signal processing
More information3. Determine whether f is a function from the set of all bit strings to the set of integers if
Exercises Exercises 1. Why is f not a function from R to R if a) f(x) = 1/x? b) c) 2. Determine whether f is a function from Z to R if a) f(n) = ± n. b). c) f(n) = 1/(n2 4). 3. Determine whether f is a
More informationOutline. Foundations of Computer Graphics (Spring 2012)
Foundations of Computer Graphics (Spring 2012) CS 184, Lectures 19: Sampling and Reconstruction http://inst.eecs.berkeley.edu/~cs184 Basic ideas of sampling, reconstruction, aliasing Signal processing
More informationIntroduction to Computer Vision. Week 3, Fall 2010 Instructor: Prof. Ko Nishino
Introduction to Computer Vision Week 3, Fall 2010 Instructor: Prof. Ko Nishino Last Week! Image Sensing " Our eyes: rods and cones " CCD, CMOS, Rolling Shutter " Sensing brightness and sensing color! Projective
More informationFrom multiple images to catalogs
Lecture 14 From multiple images to catalogs Image reconstruction Optimal co-addition Sampling-reconstruction-resampling Resolving faint galaxies Automated object detection Photometric catalogs Deep CCD
More informationReflectance & Lighting
Reflectance & Lighting Computer Vision I CSE5A Lecture 6 Last lecture in a nutshell Need for lenses (blur from pinhole) Thin lens equation Distortion and aberrations Vignetting CS5A, Winter 007 Computer
More informationDigital Image Fundamentals II
Digital Image Fundamentals II 1. Image modeling and representations 2. Pixels and Pixel relations 3. Arithmetic operations of images 4. Image geometry operation 5. Image processing with Matlab - Image
More informationDistributed Ray Tracing
Distributed Ray Tracing 1996-2018 Josef Pelikán CGG MFF UK Praha pepca@cgg.mff.cuni.cz http://cgg.mff.cuni.cz/~pepca/ DistribRT 2018 Josef Pelikán, http://cgg.mff.cuni.cz/~pepca 1 / 24 Distributed ray
More informationOptical Sectioning. Bo Huang. Pharmaceutical Chemistry
Optical Sectioning Bo Huang Pharmaceutical Chemistry Approaches to 3D imaging Physical cutting Technical difficulty Highest resolution Highest sensitivity Optical sectioning Simple sample prep. No physical
More informationVU Signal and Image Processing. Image Restoration. Torsten Möller + Hrvoje Bogunović + Raphael Sahann
052600 VU Signal and Image Processing Image Restoration Torsten Möller + Hrvoje Bogunović + Raphael Sahann torsten.moeller@univie.ac.at hrvoje.bogunovic@meduniwien.ac.at raphael.sahann@univie.ac.at vda.cs.univie.ac.at/teaching/sip/17s/
More informationLecture Image Enhancement and Spatial Filtering
Lecture Image Enhancement and Spatial Filtering Harvey Rhody Chester F. Carlson Center for Imaging Science Rochester Institute of Technology rhody@cis.rit.edu September 29, 2005 Abstract Applications of
More informationFourier transform of images
Fourier transform of images Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 2014 2015 Extension to bidimensional domain The concepts
More informationDrawing a Triangle (and an introduction to sampling)
Lecture 4: Drawing a Triangle (and an introduction to sampling) Computer Graphics CMU 15-462/15-662, Spring 2017 Assignment 1 is out! https://15462-s17.github.io/asst1_drawsvg/ Let s draw some triangles
More informationTomography. Forward projectionsp θ (r) are known as a Radon transform. Objective: reverse this process to form the original image
C. A. Bouman: Digital Image Processing - January 9, 217 1 Tomography Many medical imaging systems can only measure projections through an object with density f(x,y). Projections must be collected at every
More informationSuper-resolution Algorithm for Passive Millimeter Wave Imaging Based on Maximum Likelihood and Neighbor Wavelet Transform
Sensors & Transducers 013 by IFSA http://www.sensorsportal.com Super-resolution Algorithm for Passive Millimeter Wave Imaging Based on Maximum Lielihood and Neighbor Wavelet Transform Yiming Niu, Can Cui,
More informationImage Restoration by Revised Bayesian-Based Iterative Method
ADVCOMP 2011 : The Fifth International Conference on Advanced Engineering Computing and Applications in Sciences Image Restoration by Revised Bayesian-Based Iterative Method Sigeru Omatu, Hideo Araki Osaka
More informationCSE 455: Computer Vision Winter 2007
CSE 455: Computer Vision Winter 2007 Instructor: Professor Linda Shapiro (shapiro@cs) Additional Instructor: Dr. Matthew Brown (brown@microsoft.com) TAs: Masa Kobashi (mkbsh@cs) Peter Davis (pediddle@cs)
More informationDigital Image Processing. Week 2
Geometric spatial transformations and image registration - modify the spatial relationship between pixels in an image - these transformations are often called rubber-sheet transformations (analogous to
More informationAn Improved Fourier-based Sub-pixel Image Registration Algorithm for Raw Image Sequence of LASIS
An Improved Fourier-based Sub-pixel Image Registration Algorithm for Raw Image Sequence of LASIS MA Xiaolong *a, YANG Jianfeng a, QIAO Weidong a, XUE Bin a a Xi'an Institute of Optics and Precision Mechanics
More informationImage Acquisition + Histograms
Image Processing - Lesson 1 Image Acquisition + Histograms Image Characteristics Image Acquisition Image Digitization Sampling Quantization Histograms Histogram Equalization What is an Image? An image
More informationRegularization by multigrid-type algorithms
Regularization by multigrid-type algorithms Marco Donatelli Department of Physics and Mathematics University of Insubria Joint work with S. Serra-Capizzano Outline 1 Restoration of blurred and noisy images
More informationExtending the depth of field in a compound-eye imaging system with super-resolution reconstruction. Title. Chan, WS; Lam, EY; Ng, MK
Title Extending the depth of field in a compound-eye imaging system with super-resolution reconstruction Author(s) Chan, WS; Lam, EY; Ng, MK Citation Proceedings - International Conference On Pattern Recognition,
More informationFor 3CCD/3CMOS/4CCD Line Scan Cameras. Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras
BV-L series lenses For 3CCD/3CMOS/4CCD Line Scan Cameras Common Features Designed to be suitable for PRISM based 3CCD/CMOS/4CCD line scan cameras New optics design to improve the longitudinal chromatic
More informationUnit - I Computer vision Fundamentals
Unit - I Computer vision Fundamentals It is an area which concentrates on mimicking human vision systems. As a scientific discipline, computer vision is concerned with the theory behind artificial systems
More informationLecture 6: Edge Detection
#1 Lecture 6: Edge Detection Saad J Bedros sbedros@umn.edu Review From Last Lecture Options for Image Representation Introduced the concept of different representation or transformation Fourier Transform
More informationProjective geometry for Computer Vision
Department of Computer Science and Engineering IIT Delhi NIT, Rourkela March 27, 2010 Overview Pin-hole camera Why projective geometry? Reconstruction Computer vision geometry: main problems Correspondence
More informationBasics. Sampling and Reconstruction. Sampling and Reconstruction. Outline. (Spatial) Aliasing. Advanced Computer Graphics (Fall 2010)
Advanced Computer Graphics (Fall 2010) CS 283, Lecture 3: Sampling and Reconstruction Ravi Ramamoorthi http://inst.eecs.berkeley.edu/~cs283/fa10 Some slides courtesy Thomas Funkhouser and Pat Hanrahan
More informationDigital Signal Processing Lecture Notes 22 November 2010
Digital Signal Processing Lecture otes 22 ovember 2 Topics: Discrete Cosine Transform FFT Linear and Circular Convolution Rate Conversion Includes review of Fourier transforms, properties of Fourier transforms,
More informationEdges, interpolation, templates. Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth)
Edges, interpolation, templates Nuno Vasconcelos ECE Department, UCSD (with thanks to David Forsyth) Edge detection edge detection has many applications in image processing an edge detector implements
More informationEdge and local feature detection - 2. Importance of edge detection in computer vision
Edge and local feature detection Gradient based edge detection Edge detection by function fitting Second derivative edge detectors Edge linking and the construction of the chain graph Edge and local feature
More informationCHAPTER 3 IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN
CHAPTER 3 IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN CHAPTER 3: IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN Principal objective: to process an image so that the result is more suitable than the original image
More informationIntroduction to Sampled Signals and Fourier Transforms
Introduction to Sampled Signals and Fourier Transforms Physics116C, 4/28/06 D. Pellett References: Essick, Advanced LabVIEW Labs Press et al., Numerical Recipes, Ch. 12 Brigham, The Fast Fourier Transform
More informationCS 548: Computer Vision and Image Processing Digital Image Basics. Spring 2016 Dr. Michael J. Reale
CS 548: Computer Vision and Image Processing Digital Image Basics Spring 2016 Dr. Michael J. Reale HUMAN VISION Introduction In Computer Vision, we are ultimately trying to equal (or surpass) the human
More informationComputer Assisted Image Analysis TF 3p and MN1 5p Lecture 1, (GW 1, )
Centre for Image Analysis Computer Assisted Image Analysis TF p and MN 5p Lecture, 422 (GW, 2.-2.4) 2.4) 2 Why put the image into a computer? A digital image of a rat. A magnification of the rat s nose.
More informationFall 2015 Dr. Michael J. Reale
CS 49: Computer Vision MIDTERM REVIEW Fall 25 Dr. Michael J. Reale Midterm Review The Midterm will cover: REVIEW slide decks (inclusive) Quizzes through 4 (inclusive) REVIEW : INTRODUCTION Basic Terms
More informationBLUR INVARIANT REGISTRATION OF ROTATED, SCALED AND SHIFTED IMAGES
BLUR INVARIANT REGISTRATION OF ROTATED, SCALED AND SHIFTED IMAGES Ville Ojansivu and Janne Heikkilä Machine Vision Group, Department of Electrical and Information Engineering University of Oulu, PO Box
More informationLecture 6 Basic Signal Processing
Lecture 6 Basic Signal Processing Copyright c1996, 1997 by Pat Hanrahan Motivation Many aspects of computer graphics and computer imagery differ from aspects of conventional graphics and imagery because
More informationFourier transforms and convolution
Fourier transforms and convolution (without the agonizing pain) CS/CME/BioE/Biophys/BMI 279 Oct. 26, 2017 Ron Dror 1 Why do we care? Fourier transforms Outline Writing functions as sums of sinusoids The
More informationDistributed Ray Tracing
CT5510: Computer Graphics Distributed Ray Tracing BOCHANG MOON Distributed Ray Tracing Motivation The classical ray tracing produces very clean images (look fake) Perfect focus Perfect reflections Sharp
More information