CS 775: Advanced Computer Graphics. Lecture 12 : Antialiasing

Size: px
Start display at page:

Download "CS 775: Advanced Computer Graphics. Lecture 12 : Antialiasing"

Transcription

1 CS 775: Advanced Computer Graphics Lecture 12 : Antialiasing

2 Antialiasing How to prevent aliasing? Prefiltering Analytic Approximate Postfiltering Supersampling Stochastic Supersampling Antialiasing Textures Other forms of aliasing and antialiasing Colour Time

3 Antialiasing Prefiltering f x Prefilter Prefilter or smoothen the image so that the high-frequency components are removed before sampling. I ' x R w This is infeasible in a CG context (why?)

4 Antialiasing Prefiltering Prefiltering is infeasible in a CG context because discrete sampling is inherent in a pixel-based image model. Sampling and reconstrucion in CG is calculating a value at the centre of a pixel and then assigning that value to the entire spatial extent of that pixel So we cannot filter before sampling as we can do with convential continuous signals.

5 Antialiasing Prefiltering A1 A3 I = I1A1+I2A2+I3A3+IBAB Analytic solution Convolve with a filter at every pixel. A2 AB We can do subpixel sampling. Practical solution -This can be incorporated in the scanline rasterization process using the Abuffer (antialiased, area-averaged accumulation buffer). Make polygon masks, approximate and accumulate coverage.

6 Antialiasing Prefiltering That said, digital cameras can include optical filters to prefilter the image before capturing it on a CCD sensor. This can help bandlimit the analog optical intensity function captured by the camera and prevent some aliasing. More on this if you study computer vision/image processing.

7 Antialiasing Supersampling (or Postfilterng) reducing x here Supersampling increases the sampling rate and thus removes aliasing. increases 1/ x here R w Does this always work? What changes when we are working with images?

8 Fourier Theory Fourier Transforms in 2D Since images can be treated as 2D signals With the intensity being the signal value at a particular (x, y) pixel location. i 2 ux uy F u, v = f x, y e dxdy But images are also discrete samples of the continuous world and hence we use a discrete form of the Fourier transform to work with images. N 1 N 1 1 F u, v = 2 N x=0 i 2 f x, y e ux uy N N y=0

9 Fourier Theory Fourier Transform in 2D Remember that Fourier Spectra plots are Magnitude plots M = F u, v = R 2 u, v I 2 u, v 1/ 2

10 Fourier Theory Fourier Transform in 2D - Filters

11 Antialiasing Supersampling For supersampling, we assumed a bandlimited signal. What if the original signal has infinite support? There is no way to bandlimit frequencies that can appear in an image.

12 Antialiasing Supersampling In reality, there is no way to restrict the frequencies that may appear in an image during formation. Consider a square image made of nxn pixels of side Δx. Then the Nyquist limit of the pixel is 1/(2Δx). Frequencies higher than one cycle every two pixels will cause aliasing

13 Antialiasing Supersampling

14 Antialiasing Supersampling Create a higher resolution image and combine samples I pixel = w s. I s s Instead of one sample per pixel Take more samples per pixel Then combine the sample to calculate the intensity at the pixel

15 Antialiasing Supersampling This pushes aliasing to higher frequencies. nxn pixels of side x fmax=1/2 x 2nx2n pixels of side x/2 fmax=1/ x

16 Antialiasing Supersampling Supersampling reduces (but does not remove) anti-aliasing in images. Often create a higher resolution image, lowpass filter (i.e., blur) and then downsample.

17 Antialiasing Stochastic Supersampling Regular super sampling Adaptive super sampling Jitterred super sampling Types of Noise patterns that can be used for sampling Poisson, Poisson Disk and Jitterred. Watt, A. and Watt, M Advanced Animation and Rendering Techniques

18 Antialiasing Stochastic Supersampling Stochastic sampling is considered better as the eye is less sensitive to the presence of high frequency noise than aliases. Intuition: Yellot, 1983 Distribution of photoreceptors in the eye is like a poisson disk. Yellott, J. I. Jr (1983). Spectral consequences of photoreceptor sampling in the rhesus retina. Science, 221,

19 Antialiasing - FSAA Full scene antialiasing is common in modern graphics cards. Controlled by the display driver. Supersampling can also be controlled by the application. For e.g., while playing a game the game engine may increase or decrease the levels of antialiasing.

20 Antialiasing in Half-Life 2

Antialiasing and Related Issues

Antialiasing and Related Issues Antialiasing and Related Issues OUTLINE: Antialiasing Prefiltering, Supersampling, Stochastic Sampling Rastering and Reconstruction Gamma Correction Antialiasing Methods To reduce aliasing, either: 1.

More information

Aliasing and Antialiasing. What is Aliasing? What is Aliasing? What is Aliasing?

Aliasing 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 information

Computer Graphics (Fall 2011) Outline. CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi

Computer 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 information

Antialiasing & Compositing

Antialiasing & Compositing Antialiasing & Compositing CS4620 Lecture 14 Cornell CS4620/5620 Fall 2013 Lecture 14 (with previous instructors James/Bala, and some slides courtesy Leonard McMillan) 1 Pixel coverage Antialiasing and

More information

Sampling Theory. CS5625 Lecture Steve Marschner. Cornell CS5625 Spring 2016 Lecture 7

Sampling 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 information

Sampling and Reconstruction

Sampling and Reconstruction Sampling and reconstruction COMP 575/COMP 770 Fall 2010 Stephen J. Guy 1 Review What is Computer Graphics? Computer graphics: The study of creating, manipulating, and using visual images in the computer.

More information

Sampling and Pyramids

Sampling and Pyramids Sampling and Pyramids 15-463: Rendering and Image Processing Alexei Efros with lots of slides from Steve Seitz Today Sampling Nyquist Rate Antialiasing Gaussian and Laplacian Pyramids 1 Fourier transform

More information

Module 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture:

Module 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture: The Lecture Contains: Effect of Temporal Aperture: Spatial Aperture: Effect of Display Aperture: file:///d /...e%20(ganesh%20rana)/my%20course_ganesh%20rana/prof.%20sumana%20gupta/final%20dvsp/lecture18/18_1.htm[12/30/2015

More information

ELEC Dr Reji Mathew Electrical Engineering UNSW

ELEC 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 information

Filters. Materials from Prof. Klaus Mueller

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 information

Last Lecture. photomatix.com

Last 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 information

Last Lecture. photomatix.com

Last 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 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 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 information

To Do. Advanced Computer Graphics. Image Compositing. Digital Image Compositing. Outline. Blue Screen Matting

To Do. Advanced Computer Graphics. Image Compositing. Digital Image Compositing. Outline. Blue Screen Matting Advanced Computer Graphics CSE 163 [Spring 2018], Lecture 5 Ravi Ramamoorthi http://www.cs.ucsd.edu/~ravir To Do Assignment 1, Due Apr 27. This lecture only extra credit and clear up difficulties Questions/difficulties

More information

Image Processing. Image Processing. What is an Image? Image Resolution. Overview. Sources of Error. Filtering Blur Detect edges

Image 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 information

Sampling of Continuous-Time Signals. Reference chapter 4 in Oppenheim and Schafer.

Sampling of Continuous-Time Signals. Reference chapter 4 in Oppenheim and Schafer. Sampling of Continuous-Time Signals Reference chapter 4 in Oppenheim and Schafer. Periodic Sampling of Continuous Signals T = sampling period fs = sampling frequency when expressing frequencies in radians

More information

Anti aliasing and Graphics Formats

Anti aliasing and Graphics Formats Anti aliasing and Graphics Formats Eric C. McCreath School of Computer Science The Australian National University ACT 0200 Australia ericm@cs.anu.edu.au Overview 2 Nyquist sampling frequency supersampling

More information

Lecture 3 Review of Signals and Systems: Part 2. EE4900/EE6720 Digital Communications

Lecture 3 Review of Signals and Systems: Part 2. EE4900/EE6720 Digital Communications EE4900/EE6720: Digital Communications 1 Lecture 3 Review of Signals and Systems: Part 2 Block Diagrams of Communication System Digital Communication System 2 Informatio n (sound, video, text, data, ) Transducer

More information

CS101 Lecture 19: Digital Images. John Magee 18 July 2013 Some material copyright Jones and Bartlett. Overview/Questions

CS101 Lecture 19: Digital Images. John Magee 18 July 2013 Some material copyright Jones and Bartlett. Overview/Questions CS101 Lecture 19: Digital Images John Magee 18 July 2013 Some material copyright Jones and Bartlett 1 Overview/Questions What is digital information? What is color? How do pictures get encoded into binary

More information

Sampling and pixels. CS 178, Spring Marc Levoy Computer Science Department Stanford University. Begun 4/23, finished 4/25.

Sampling 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 information

General Imaging System

General Imaging System General Imaging System Lecture Slides ME 4060 Machine Vision and Vision-based Control Chapter 5 Image Sensing and Acquisition By Dr. Debao Zhou 1 2 Light, Color, and Electromagnetic Spectrum Penetrate

More information

CS4495/6495 Introduction to Computer Vision. 2C-L3 Aliasing

CS4495/6495 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 information

Sampling and reconstruction. CS 4620 Lecture 13

Sampling and reconstruction. CS 4620 Lecture 13 Sampling and reconstruction CS 4620 Lecture 13 Lecture 13 1 Outline Review signal processing Sampling Reconstruction Filtering Convolution Closely related to computer graphics topics such as Image processing

More information

Photo-Consistent Motion Blur Modeling for Realistic Image Synthesis

Photo-Consistent Motion Blur Modeling for Realistic Image Synthesis Photo-Consistent Motion Blur Modeling for Realistic Image Synthesis Huei-Yung Lin and Chia-Hong Chang Department of Electrical Engineering, National Chung Cheng University, 168 University Rd., Min-Hsiung

More information

Final Exam Solutions June 7, 2004

Final 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 information

CS101 Lecture 12: Digital Images. What You ll Learn Today

CS101 Lecture 12: Digital Images. What You ll Learn Today CS101 Lecture 12: Digital Images Sampling and Quantizing Using bits to Represent Colors and Images Aaron Stevens (azs@bu.edu) 20 February 2013 What You ll Learn Today What is digital information? How to

More information

Midterm is on Thursday!

Midterm 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 information

digital film technology Resolution Matters what's in a pattern white paper standing the test of time

digital film technology Resolution Matters what's in a pattern white paper standing the test of time digital film technology Resolution Matters what's in a pattern white paper standing the test of time standing the test of time An introduction >>> Film archives are of great historical importance as they

More information

Laboratory Manual 2, MSPS. High-Level System Design

Laboratory Manual 2, MSPS. High-Level System Design No Rev Date Repo Page 0002 A 2011-09-07 MSPS 1 of 16 Title High-Level System Design File MSPS_0002_LM_matlabSystem_A.odt Type EX -- Laboratory Manual 2, Area MSPS ES : docs : courses : msps Created Per

More information

Dynamically Reparameterized Light Fields & Fourier Slice Photography. Oliver Barth, 2009 Max Planck Institute Saarbrücken

Dynamically Reparameterized Light Fields & Fourier Slice Photography. Oliver Barth, 2009 Max Planck Institute Saarbrücken Dynamically Reparameterized Light Fields & Fourier Slice Photography Oliver Barth, 2009 Max Planck Institute Saarbrücken Background What we are talking about? 2 / 83 Background What we are talking about?

More information

Design IV. E232 Spring 07

Design IV. E232 Spring 07 Design IV Spring 07 Class 8 Bruce McNair bmcnair@stevens.edu 8-1/38 Computerized Data Acquisition Measurement system architecture System under test sensor sensor sensor sensor signal conditioning signal

More information

Texture mapping from 0 to infinity

Texture mapping from 0 to infinity Announcements CS4620/5620: Lecture 24 HW 3 out Barycentric coordinates for Problem 1 Texture Mapping 1 2 Texture mapping from 0 to infinity When you go close... When viewed from a distance Aliasing! 3

More information

Data acquisition and instrumentation. Data acquisition

Data acquisition and instrumentation. Data acquisition Data acquisition and instrumentation START Lecture Sam Sadeghi Data acquisition 1 Humanistic Intelligence Body as a transducer,, data acquisition and signal processing machine Analysis of physiological

More information

Fourier transforms, SIM

Fourier 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 information

06: Thinking in Frequencies. CS 5840: Computer Vision Instructor: Jonathan Ventura

06: 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 information

Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester

Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester Lecture 6: Image Acquisition and Digitization 14.10.2017 Dr. Mohammed Abdel-Megeed

More information

digital film technology Scanity multi application film scanner white paper

digital film technology Scanity multi application film scanner white paper digital film technology Scanity multi application film scanner white paper standing the test of time multi application film scanner Scanity >>> In the last few years, both digital intermediate (DI) postproduction

More information

ECE 484 Digital Image Processing Lec 09 - Image Resampling

ECE 484 Digital Image Processing Lec 09 - Image Resampling ECE 484 Digital Image Processing Lec 09 - Image Resampling Zhu Li Dept of CSEE, UMKC Office: FH560E, Email: lizhu@umkc.edu, Ph: x 2346. http://l.web.umkc.edu/lizhu slides created with WPS Office Linux

More information

Digital Photogrammetry. Presented by: Dr. Hamid Ebadi

Digital Photogrammetry. Presented by: Dr. Hamid Ebadi Digital Photogrammetry Presented by: Dr. Hamid Ebadi Background First Generation Analog Photogrammetry Analytical Photogrammetry Digital Photogrammetry Photogrammetric Generations 2000 digital photogrammetry

More information

Sampling and Reconstruction of Analog Signals

Sampling and Reconstruction of Analog Signals Sampling and Reconstruction of Analog Signals Chapter Intended Learning Outcomes: (i) Ability to convert an analog signal to a discrete-time sequence via sampling (ii) Ability to construct an analog signal

More information

Image Filtering and Gaussian Pyramids

Image 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 information

Recall. Sampling. Why discrete time? Why discrete time? Many signals are continuous-time signals Light Object wave CCD

Recall. Sampling. Why discrete time? Why discrete time? Many signals are continuous-time signals Light Object wave CCD Recall Many signals are continuous-time signals Light Object wave CCD Sampling mic Lens change of voltage change of voltage 2 Why discrete time? With the advance of computer technology, we want to process

More information

CS 465 Prelim 1. Tuesday 4 October hours. Problem 1: Image formats (18 pts)

CS 465 Prelim 1. Tuesday 4 October hours. Problem 1: Image formats (18 pts) CS 465 Prelim 1 Tuesday 4 October 2005 1.5 hours Problem 1: Image formats (18 pts) 1. Give a common pixel data format that uses up the following numbers of bits per pixel: 8, 16, 32, 36. For instance,

More information

Color and perception Christian Miller CS Fall 2011

Color and perception Christian Miller CS Fall 2011 Color and perception Christian Miller CS 354 - Fall 2011 A slight detour We ve spent the whole class talking about how to put images on the screen What happens when we look at those images? Are there any

More information

Computer Graphics. - Display and Imaging Devices - Hendrik Lensch. Computer Graphics WS07/08 Display and Imaging Devices

Computer Graphics. - Display and Imaging Devices - Hendrik Lensch. Computer Graphics WS07/08 Display and Imaging Devices Computer Graphics - Display and Imaging Devices - Hendrik Lensch Overview Last Week Volume Rendering Today Display and Imaging Devices Exam Monday, 18 th please be there at 8:00 sharp starts at 8:15 will

More information

CMPT 318: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals

CMPT 318: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals CMPT 318: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 16, 2006 1 Continuous vs. Discrete

More information

PROBLEM SET 6. Note: This version is preliminary in that it does not yet have instructions for uploading the MATLAB problems.

PROBLEM SET 6. Note: This version is preliminary in that it does not yet have instructions for uploading the MATLAB problems. PROBLEM SET 6 Issued: 2/32/19 Due: 3/1/19 Reading: During the past week we discussed change of discrete-time sampling rate, introducing the techniques of decimation and interpolation, which is covered

More information

Continuous vs. Discrete signals. Sampling. Analog to Digital Conversion. CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals

Continuous vs. Discrete signals. Sampling. Analog to Digital Conversion. CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Continuous vs. Discrete signals CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 22,

More information

Introduction to DSP ECE-S352 Fall Quarter 2000 Matlab Project 1

Introduction to DSP ECE-S352 Fall Quarter 2000 Matlab Project 1 Objective: Introduction to DSP ECE-S352 Fall Quarter 2000 Matlab Project 1 This Matlab Project is an extension of the basic correlation theory presented in the course. It shows a practical application

More information

CSCI 1290: Comp Photo

CSCI 1290: Comp Photo CSCI 29: Comp Photo Fall 28 @ Brown University James Tompkin Many slides thanks to James Hays old CS 29 course, along with all of its acknowledgements. Things I forgot on Thursday Grads are not required

More information

Sensing Increased Image Resolution Using Aperture Masks

Sensing Increased Image Resolution Using Aperture Masks Sensing Increased Image Resolution Using Aperture Masks Ankit Mohan, Xiang Huang, Jack Tumblin Northwestern University Ramesh Raskar MIT Media Lab CVPR 2008 Supplemental Material Contributions Achieve

More information

Midterm Examination CS 534: Computational Photography

Midterm Examination CS 534: Computational Photography Midterm Examination CS 534: Computational Photography November 3, 2015 NAME: SOLUTIONS Problem Score Max Score 1 8 2 8 3 9 4 4 5 3 6 4 7 6 8 13 9 7 10 4 11 7 12 10 13 9 14 8 Total 100 1 1. [8] What are

More information

What will be on the midterm?

What will be on the midterm? What will be on the midterm? CS 178, Spring 2014 Marc Levoy Computer Science Department Stanford University General information 2 Monday, 7-9pm, Cubberly Auditorium (School of Edu) closed book, no notes

More information

SAMPLING THEORY. Representing continuous signals with discrete numbers

SAMPLING 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 information

Intorduction to light sources, pinhole cameras, and lenses

Intorduction to light sources, pinhole cameras, and lenses Intorduction to light sources, pinhole cameras, and lenses Erik G. Learned-Miller Department of Computer Science University of Massachusetts, Amherst Amherst, MA 01003 October 26, 2011 Abstract 1 1 Analyzing

More information

Sampling and reconstruction

Sampling and reconstruction Sampling and reconstruction CS 5625 Lecture 6 Lecture 6 1 Sampled representations How to store and compute with continuous functions? Common scheme for representation: samples write down the function s

More information

קורס 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 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 information

CS 262 Lecture 01: Digital Images and Video. John Magee Some material copyright Jones and Bartlett

CS 262 Lecture 01: Digital Images and Video. John Magee Some material copyright Jones and Bartlett CS 262 Lecture 01: Digital Images and Video John Magee Some material copyright Jones and Bartlett 1 Overview/Questions What is digital information? What is color? How do pictures get encoded into binary

More information

Determination of the MTF of JPEG Compression Using the ISO Spatial Frequency Response Plug-in.

Determination of the MTF of JPEG Compression Using the ISO Spatial Frequency Response Plug-in. IS&T's 2 PICS Conference IS&T's 2 PICS Conference Copyright 2, IS&T Determination of the MTF of JPEG Compression Using the ISO 2233 Spatial Frequency Response Plug-in. R. B. Jenkin, R. E. Jacobson and

More information

2D Discrete Fourier Transform

2D 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 information

a marriage between Film and Video Viper FilmStream Camera: A Technical Overview Abstract Introduction

a marriage between Film and Video Viper FilmStream Camera: A Technical Overview Abstract Introduction Jan van Rooy, Peter Centen, Mike Stekelenburg Abstract This paper proposes a camera for a new workflow in which picture data from the CCDs of the camera are transferred directly into postproduction, maintaining

More information

Evaluating Commercial Scanners for Astronomical Images. The underlying technology of the scanners: Pixel sizes:

Evaluating Commercial Scanners for Astronomical Images. The underlying technology of the scanners: Pixel sizes: Evaluating Commercial Scanners for Astronomical Images Robert J. Simcoe Associate Harvard College Observatory rjsimcoe@cfa.harvard.edu Introduction: Many organizations have expressed interest in using

More information

Digital Cameras The Imaging Capture Path

Digital Cameras The Imaging Capture Path Manchester Group Royal Photographic Society Imaging Science Group Digital Cameras The Imaging Capture Path by Dr. Tony Kaye ASIS FRPS Silver Halide Systems Exposure (film) Processing Digital Capture Imaging

More information

Cameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017

Cameras. Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017 Cameras Steve Rotenberg CSE168: Rendering Algorithms UCSD, Spring 2017 Camera Focus Camera Focus So far, we have been simulating pinhole cameras with perfect focus Often times, we want to simulate more

More information

6.003: Signals and Systems. Sampling

6.003: Signals and Systems. Sampling 6.003: Signals and Systems Sampling April 27, 200 Mid-term Examination #3 om orrow: W ednesday, A pril 2 8, 7 : 3 0-9 : 3 0 pm. No recitations tomorrow. Coverage: Lectures 20 Recitations 20 Homeworks Homework

More information

The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?

The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do? Computational Photography The ultimate camera What does it do? Image from Durand & Freeman s MIT Course on Computational Photography Today s reading Szeliski Chapter 9 The ultimate camera Infinite resolution

More information

CS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale

CS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale CS 548: Computer Vision REVIEW: Digital Image Basics Spring 2016 Dr. Michael J. Reale Human Vision System: Cones and Rods Two types of receptors in eye: Cones Brightness and color Photopic vision = bright-light

More information

Image Processing. What is an image? קורס גרפיקה ממוחשבת 2008 סמסטר ב' Converting to digital form. Sampling and Reconstruction.

Image 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 information

Digital Processing of Continuous-Time Signals

Digital Processing of Continuous-Time Signals Chapter 4 Digital Processing of Continuous-Time Signals 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 4-1-1 Digital Processing of Continuous-Time Signals Digital

More information

DIGITAL CAMERA SENSORS

DIGITAL CAMERA SENSORS DIGITAL CAMERA SENSORS Bill Betts March 21, 2018 Camera Sensors The soul of a digital camera is its sensor - to determine image size, resolution, lowlight performance, depth of field, dynamic range, lenses

More information

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the

More information

CS6670: Computer Vision Noah Snavely. Administrivia. Administrivia. Reading. Last time: Convolution. Last time: Cross correlation 9/8/2009

CS6670: 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

Image Processing. COMP 3072 / GV12 Gabriel Brostow. TA: Josias P. Elisee (with help from Dr Wole Oyekoya) Image Processing.

Image Processing. COMP 3072 / GV12 Gabriel Brostow. TA: Josias P. Elisee (with help from Dr Wole Oyekoya) Image Processing. Image Processing COMP 3072 / GV12 Gabriel Brostow TA: Josias P. Elisee (with help from Dr Wole Oyekoya) 1 2 Motivation and Goals Grounding in image processing techniques Concentrate on algorithms used

More information

Lecture 2: Digital Image Fundamentals -- Sampling & Quantization

Lecture 2: Digital Image Fundamentals -- Sampling & Quantization I2200: Digital Image processing Lecture 2: Digital Image Fundamentals -- Sampling & Quantization Prof. YingLi Tian Sept. 6, 2017 Department of Electrical Engineering The City College of New York The City

More information

Digital Halftoning Using Two-Dimensional Carriers with a Noninteger Period

Digital Halftoning Using Two-Dimensional Carriers with a Noninteger Period Digital Halftoning Using Two-Dimensional Carriers with a Noninteger Period Thomas Scheermesser, Frank Wyrowski*, Olof Bryngdahl University of Essen, Physics Department, 45117 Essen, Germany Abstract Among

More information

Digital Processing of

Digital Processing of Chapter 4 Digital Processing of Continuous-Time Signals 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 4-1-1 Digital Processing of Continuous-Time Signals Digital

More information

USE OF FT IN IMAGE PROCESSING IMAGE PROCESSING (RRY025)

USE 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 information

CSE 527: Introduction to Computer Vision

CSE 527: Introduction to Computer Vision CSE 527: Introduction to Computer Vision Week 7 - Class 2: Segmentation 2 October 12th, 2017 Today Segmentation, continued: - Superpixels Graph-cut methods Mid-term: - Practice questions Administrations

More information

Lecture Schedule: Week Date Lecture Title

Lecture Schedule: Week Date Lecture Title http://elec3004.org Sampling & More 2014 School of Information Technology and Electrical Engineering at The University of Queensland Lecture Schedule: Week Date Lecture Title 1 2-Mar Introduction 3-Mar

More information

Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals

Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego October 3, 2016 1 Continuous vs. Discrete signals

More information

Double resolution from a set of aliased images

Double resolution from a set of aliased images Double resolution from a set of aliased images Patrick Vandewalle 1,SabineSüsstrunk 1 and Martin Vetterli 1,2 1 LCAV - School of Computer and Communication Sciences Ecole Polytechnique Fédérale delausanne(epfl)

More information

Computer Graphics Si Lu Fall /25/2017

Computer Graphics Si Lu Fall /25/2017 Computer Graphics Si Lu Fall 2017 09/25/2017 Today Course overview and information Digital images Homework 1 due Oct. 4 in class No late homework will be accepted 2 Pre-Requisites C/C++ programming Linear

More information

Image Representations, Colors, & Morphing. Stephen J. Guy Comp 575

Image Representations, Colors, & Morphing. Stephen J. Guy Comp 575 Image Representations, Colors, & Morphing Stephen J. Guy Comp 575 Procedural Stuff How to make a webpage Assignment 0 grades New office hours Dinesh Teaching Next week ray-tracing Problem set Review Overview

More information

Sampling and Reconstruction. Today: Color Theory. Color Theory COMP575

Sampling and Reconstruction. Today: Color Theory. Color Theory COMP575 and COMP575 Today: Finish up Color Color Theory CIE XYZ color space 3 color matching functions: X, Y, Z Y is luminance X and Z are color values WP user acdx Color Theory xyy color space Since Y is luminance,

More information

Head Movement Based Temporal Antialiasing for VR HMDs

Head Movement Based Temporal Antialiasing for VR HMDs Head Movement Based Temporal Antialiasing for VR HMDs Jung-Bum Kim Soo-Ryum Choi Joon-Hyun Choi Sang-Jun Ahn Chan-Min Park Samsung Electronics {jb83.kim, sooryum.choi, jh53.choi, sjun.ahn, chanmin.park}@samsung.com

More information

Lecture 30: Image Sensors (Cont) Computer Graphics and Imaging UC Berkeley CS184/284A

Lecture 30: Image Sensors (Cont) Computer Graphics and Imaging UC Berkeley CS184/284A Lecture 30: Image Sensors (Cont) Computer Graphics and Imaging UC Berkeley Reminder: The Pixel Stack Microlens array Color Filter Anti-Reflection Coating Stack height 4um is typical Pixel size 2um is typical

More information

Sharpness, Resolution and Interpolation

Sharpness, Resolution and Interpolation Sharpness, Resolution and Interpolation Introduction There are a lot of misconceptions about resolution, camera pixel count, interpolation and their effect on astronomical images. Some of the confusion

More information

6.003: Signal Processing. Synthetic Aperture Optics

6.003: Signal Processing. Synthetic Aperture Optics 6.003: Signal Processing Synthetic Aperture Optics December 11, 2018 Subject Evaluations Your feedback is important to us! Please give feedback to the staff and future 6.003 students: http://registrar.mit.edu/subjectevaluation

More information

Solution Set #2

Solution Set #2 05-78-0 Solution Set #. For the sampling function shown, analyze to determine its characteristics, e.g., the associated Nyquist sampling frequency (if any), whether a function sampled with s [x; x] may

More information

Digital Filters IIR (& Their Corresponding Analog Filters) Week Date Lecture Title

Digital Filters IIR (& Their Corresponding Analog Filters) Week Date Lecture Title http://elec3004.com Digital Filters IIR (& Their Corresponding Analog Filters) 2017 School of Information Technology and Electrical Engineering at The University of Queensland Lecture Schedule: Week Date

More information

ENGR 210 Lab 12: Sampling and Aliasing

ENGR 210 Lab 12: Sampling and Aliasing ENGR 21 Lab 12: Sampling and Aliasing In the previous lab you examined how A/D converters actually work. In this lab we will consider some of the consequences of how fast you sample and of the signal processing

More information

Image Formation: Camera Model

Image Formation: Camera Model Image Formation: Camera Model Ruigang Yang COMP 684 Fall 2005, CS684-IBMR Outline Camera Models Pinhole Perspective Projection Affine Projection Camera with Lenses Digital Image Formation The Human Eye

More information

Multirate DSP, part 1: Upsampling and downsampling

Multirate DSP, part 1: Upsampling and downsampling Multirate DSP, part 1: Upsampling and downsampling Li Tan - April 21, 2008 Order this book today at www.elsevierdirect.com or by calling 1-800-545-2522 and receive an additional 20% discount. Use promotion

More information

Multirate DSP, part 3: ADC oversampling

Multirate DSP, part 3: ADC oversampling Multirate DSP, part 3: ADC oversampling Li Tan - May 04, 2008 Order this book today at www.elsevierdirect.com or by calling 1-800-545-2522 and receive an additional 20% discount. Use promotion code 92562

More information

CS148: Introduction to Computer Graphics and Imaging. Displays. Topics. Spatial resolution Temporal resolution Tone mapping. Display technologies

CS148: Introduction to Computer Graphics and Imaging. Displays. Topics. Spatial resolution Temporal resolution Tone mapping. Display technologies CS148: Introduction to Computer Graphics and Imaging Displays Topics Spatial resolution Temporal resolution Tone mapping Display technologies Resolution World is continuous, digital media is discrete Three

More information

RGB RESOLUTION CONSIDERATIONS IN A NEW CMOS SENSOR FOR CINE MOTION IMAGING

RGB RESOLUTION CONSIDERATIONS IN A NEW CMOS SENSOR FOR CINE MOTION IMAGING WHITE PAPER RGB RESOLUTION CONSIDERATIONS IN A NEW CMOS SENSOR FOR CINE MOTION IMAGING Written by Larry Thorpe Professional Engineering & Solutions Division, Canon U.S.A., Inc. For more info: cinemaeos.usa.canon.com

More information

Sampling and Reconstruction

Sampling 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 information

8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and

8.2 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 information

Moving from continuous- to discrete-time

Moving from continuous- to discrete-time Moving from continuous- to discrete-time Sampling ideas Uniform, periodic sampling rate, e.g. CDs at 44.1KHz First we will need to consider periodic signals in order to appreciate how to interpret discrete-time

More information

ISO INTERNATIONAL STANDARD. Photography Electronic still-picture cameras Resolution measurements

ISO INTERNATIONAL STANDARD. Photography Electronic still-picture cameras Resolution measurements INTERNATIONAL STANDARD ISO 12233 First edition 2000-09-01 Photography Electronic still-picture cameras Resolution measurements Photographie Appareils de prises de vue électroniques Mesurages de la résolution

More information