קורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
|
|
- Anastasia Allison
- 5 years ago
- Views:
Transcription
1 קורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
2 What is an image? An image is a discrete array of samples representing a continuous 2D function Continuous function Discrete samples 2
3 Amplitude Converting to digital form Convert continuous sensed data into digital form Quantization Sampling 3
4 Sampling and Reconstruction Sampling Reconstruction
5 Sampling and Reconstruction Figure 19.9 FvDFH
6 Sampling Theory How many samples are required to represent a given signal without loss of information? What signals can be reconstructed without loss for a given sampling rate?
7 What happens when we use too few samples? Aliasing Aliasing Figure FvDFH
8 Spectral Analysis So our image (function f(x,y)) describes how the signal changes over time (x and y axes) Aliasing occurs when we use too few samples (what is enough?) The more an image changes, the more we need to sample it. How do we measure how fast a signal changes? Frequencies 8
9 Spectral Analysis Spatial domain: Function: f(x) Filtering: convolution Frequency domain: Function: F(u) Filtering: multiplication Any signal can be written as a sum of periodic functions.
10 Fourier Joseph Fourier discovered in 1822 that Any periodic function can be expressed as the sum of sines and/or cosines if different frequencies (Fourier Series) Even functions that are not periodic can be expressed as the integral of sines and/or cosines (Fourier Transform) Initial application was in heat diffusion 10
11 Fourier Transform (1D) Figure 2.6 Wolberg
12 Fourier transform: F( u) Fourier Transform (1D) f ( x) e Inverse Fourier transform: i2xu dx f ( x) F ( u) e i2ux du
13 Sampling Theorem A signal can be reconstructed from its samples, if the original signal has no frequencies above 1/2 the sampling frequency - Shannon The minimum sampling rate for bandlimited function is called Nyquist rate A signal is bandlimited if its highest frequency is bounded. The frequency is called the bandwidth.
14 Image Processing Pixel operations Add random noise Add luminance Add contrast Add saturation Filtering Blur Detect edges Sharpen Emboss Median Quantization Uniform Quantization Floyd-Steinberg dither Warping Scale Rotate Warps Combining Composite Morph
15 Adjusting Brightness Simply scale pixel components Must clamp to range (e.g., 0 to 1) Original Brighter
16 Adjusting Contrast Compute mean luminance L for all pixels luminance = 0.30*r *g *b Scale deviation from L for each pixel component Must clamp to range (e.g., 0 to 1) L Original More Contrast
17 Image Processing Pixel operations Add random noise Add luminance Add contrast Add saturation Filtering Blur Detect edges Sharpen Emboss Median Quantization Uniform Quantization Floyd-Steinberg dither Warping Scale Rotate Warps Combining Composite Morph
18 Linear Filtering (Spatial Domain) Convolution Each output pixel is a linear combination of input pixels in neighborhood with weights prescribed by a filter Filter = 18
19 Adjust Blurriness Convolve with a filter whose entries sum to one Each pixel becomes a weighted average of its neighbors Original Blur What do you think happens in the frequency domain? Filter =
20 More on blur (lowpass filters) We can either take a uniform kernel (mean filter) Or a Gaussian kernel A Gaussian kernel tends to provide gentler smoothing and preserve edges better
21 Edge Detection Convolve with a filter that finds differences between neighbor pixels Original Detect edges 1 Filter =
22 Sharpen Sum detected edges with original image Original Sharpened Filter =
23 Emboss Convolve with a filter that highlights gradients in particular directions Original Embossed Filter =
24 Non-linear filtering Any operation on a neighborhood around each pixel For example: Selecting the median value of the neighborhood Original 3x3 5x5 7x7 11x11 15x15 24
25 Image Processing Pixel operations Add random noise Add luminance Add contrast Add saturation Filtering Blur Detect edges Sharpen Emboss Median Quantization Uniform Quantization Floyd-Steinberg dither Warping Scale Rotate Warps Combining Composite Morph
26 Reduce intensity resolution Quantization Frame buffers have limited number of bits per pixel Physical devices have limited dynamic range n=0.5 26
27 P(x,y) = round(i(x,y)) Uniform Quantization I(x,y) P(x,y) 2 bits per pixel 27
28 Uniform Quantization Images with decreasing bits per pixel: 8 bits 4 bits 2 bits 1 bit 28
29 Reducing effects of Quantization Dithering Random dither Ordered dither Error diffusion dither Halftoning Classical halftoning 29
30 Dithering Distribute errors among pixels Exploit spatial integration in our eye Display greater range of perceptible intensities Original (8 bits) Uniform Quantization (1 bit) Floyd-Steinberg Dither (1 bit)
31 P(x,y) P(x,y) Randomize quantization errors Errors appear as noise Random Dither I(x,y) I(x,y) P(x, y) = trunc(i(x, y) + noise(x,y) + 0.5) 1 bit
32 Random Dither Original (8 bits) Uniform Quantization (1 bit) Random Dither (1 bit)
33 Ordered Dither Pseudo-random quantization errors Matrix stores pattern of threshholds D 2 For each pixel (x,y) oldpixel = I(x,y) +D(x mod n,y mod n) P(x,y)= find_closest_color(oldpixel)
34 Ordered Dither Bayer s ordered dither matrices D D (2,2) 4 (2,1) 4 (1,2) 4 (1,1) 4 n n n n n n n n n U D D U D D U D D U D D D Basic idea: organize successive integers such that the average distance between two successive numbers in the map is as large as possible
35 An example Ordered Dither Palette consists of 8 red tones, 8 green tones and their combinations (64 colors) Original image had colors Undithered Dithered 35
36 Ordered Dither Original (8 bits) Random Dither (1 bit) Ordered Dither (1 bit)
37 Error Diffusion Dither Spread quantization error over neighbor pixels Error dispersed to pixels right and below a b g d a b g d 1.0 Figure from H&B
38 Floyd-Steinberg Algorithm for (x = 0; x < width; x++) { for (y = 0; y < height; y++) { P(x,y) = trunc(i(x,y) + 0.5) e = I(x,y) - P(x,y) I(x,y+1) += a*e; I(x+1,y-1) += b*e; I(x+1,y) += g*e; I(x+1,y+1) += d *e; } }
39 Error Diffusion Dither Original (8 bits) Random Dither (1 bit) Ordered Dither (1 bit) Floyd-Steinberg Dither (1 bit)
40 More examples Original Threshold Random Bayer Floyd-Steinberg Jarvice, Judice & Ninke Stucki Burkes 40
41 Reducing effects of Quantization Dithering Random dither Ordered dither Error diffusion dither Halftoning Classical halftoning 41
42 Classical Halftoning Use dots of varying size to represent intensities Area of dots proportional to intensity in image I(x,y) P(x,y)
43 Classical Halftoning Newspaper Image From New York Times, 9/21/99
44 Halftone patterns Use cluster of pixels to represent intensity Trade spatial resolution for intensity resolution Figure from H&B
45 Halftone patterns How many intensities in a n x n cluster? Figure from H&B
46 Image Processing Pixel operations Add random noise Add luminance Add contrast Add saturation Filtering Blur Detect edges Sharpen Emboss Median Quantization Uniform Quantization Floyd-Steinberg dither Warping Scale Rotate Warps Combining Composite Morph
47 Image Warping Move pixels of image Warp Source image Destination image
48 Image Warping Issues How do we specify where every pixel goes? (mapping) How do we compute colors at destination pixels? (resampling) Warp Source image Destination image
49 Example Image Scaling (x,y ) = (sx*x, sy*y); I(x,y ) =? 49
50 Image Warping Image warping requires resampling of image Resampling 50
51 BACK TO SAMPLING 51
52 Aliasing (again) In general: Artifacts due to under-sampling or poor reconstruction Specifically, in graphics: Spatial aliasing Temporal aliasing Under-sampling Figure FvDFH
53 Spatial Aliasing Artifacts due to limited spatial resolution
54 Spatial Aliasing Artifacts due to limited spatial resolution Jaggies
55 Temporal Aliasing Artifacts due to limited temporal resolution Strobing Flickering
56 Temporal Aliasing Artifacts due to limited temporal resolution Strobing Flickering
57 Temporal Aliasing Artifacts due to limited temporal resolution Strobing Flickering
58 Temporal Aliasing Artifacts due to limited temporal resolution Strobing Flickering
59 Sample at higher rate Not always possible Doesn t always solve problem Antialiasing Pre-filter to form bandlimited signal Form bandlimited function (low-pass filter) Trades aliasing for blurring
60 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display
61 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Continuous Function
62 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Discrete Samples
63 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Reconstructed Function
64 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Transformed Function
65 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Bandlimited Function
66 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Discrete samples
67 Image Processing Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display Display
68 Frequency domain Ideal Bandlimiting Filter Spatial domain Sinc( x) sin x x Figure 4.5 Wolberg
69 Convolution Practical Image Processing Finite low-pass filters Point sampling (bad) Triangle filter Gaussian filter Real world Sample Discrete samples (pixels) Reconstruct Reconstructed function Transform Transformed function Filter Bandlimited function Sample Discrete samples (pixels) Reconstruct Display
70 Convolution with triangle filter Triangle Filter Input Output Figure 2.4 Wolberg
71 Gaussian Filter Convolution with Gaussian filter Input Output Figure 2.4 Wolberg
72 AND BACK TO WARPING 72
73 Image Resampling What if we are resampling a 2D image? (u,v)
74 Image Resampling Compute weighted sum of pixel neighborhood Output is weighted average dst(u,v)=0; for(ix=u-w;ix<=u+w;ix++) for(iy=v-w;iy<=v+w;iy++) d=dist between (ix,iy) and (u,v) dst(u,v) += k(ix,iy) * src(ix,iy) (u,v) W d (ix,iy)
75 Image Resampling For isotropic Triangle and Gaussian filters, k(ix,iy) is a function of d and w (u,v) W d (ix,iy)
76 Image Resampling For isotropic Triangle and Gaussian filters, k(ix,iy) is a function of d and w (u,v) W d (ix,iy)
77 Triangle Filtering (width <= 1) Bilinearly interpolate four closest pixels a = linear interpolation of src(u 1,v 2 ) and src(u 2,v 2 ) b = linear interpolation of src(u 1,v 1 ) and src(u 2,v 1 ) dst(x,y) = linear interpolation of a and b (u 1,v 2 ) a (u 2,v 2 ) (u,v) (u 1,v 1 ) b (u 2,v 1 )
78 Kernel is a Guassian function Gaussian Filtering (u,v) w 3 d (ix,iy)
79 Image Scale Scale (src, dst, sx, sy): w max(1/sx,1/sy); for (int ix = 0; ix < xmax; ix++) { for (int iy = 0; iy < ymax; iy++) { float u = ix / sx; float v = iy / sy; dst(ix,iy) = resample(src,u,v,k,w); } } v (u,v) y (x,y) u Scale 0.5 x
80 How do we resample? Point sampling Simple but causes aliasing Triangle and Gaussian Algorithm as we saw earlier Float resample(src,u,v,w) { int iu = round(u); int iv = round(v); return src(iu,iv); } 80
81 Image Warping (in General) Reverse Mapping 81
82 Image Warping (in General) Alternative (forward) 82
83 Next time? That s it for today Finishing corners on image processing Transformations and Projections Rendering 83
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 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 informationImage Processing COS 426
Image Processing COS 426 What is a Digital Image? A digital image is a discrete array of samples representing a continuous 2D function Continuous function Discrete samples Limitations on Digital Images
More informationImage Processing. Adam Finkelstein Princeton University COS 426, Spring 2019
Image Processing Adam Finkelstein Princeton University COS 426, Spring 2019 Image Processing Operations Luminance Brightness Contrast Gamma Histogram equalization Color Grayscale Saturation White balance
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 Processing. Michael Kazhdan ( /657) HB Ch FvDFH Ch. 13.1
Image Processing Michael Kazhdan (600.457/657) HB Ch. 14.4 FvDFH Ch. 13.1 Outline Human Vision Image Representation Reducing Color Quantization Artifacts Basic Image Processing Human Vision Model of Human
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 informationHuman Vision, Color and Basic Image Processing
Human Vision, Color and Basic Image Processing Connelly Barnes CS4810 University of Virginia Acknowledgement: slides by Jason Lawrence, Misha Kazhdan, Allison Klein, Tom Funkhouser, Adam Finkelstein and
More informationRaster Graphics. Overview קורס גרפיקה ממוחשבת 2008 סמסטר ב' What is an image? What is an image? Image Acquisition. Image display 5/19/2008.
Overview Images What is an image? How are images displayed? Color models How do we perceive colors? How can we describe and represent colors? קורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים
More informationקורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
קורס גרפיקה ממוחשבת 2008 סמסטר ב' Raster Graphics 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור Images What is an image? How are images displayed? Color models Overview How
More informationImage Filtering. Median Filtering
Image Filtering Image filtering is used to: Remove noise Sharpen contrast Highlight contours Detect edges Other uses? Image filters can be classified as linear or nonlinear. Linear filters are also know
More informationSampling 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 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 informationGraphics and Image Processing Basics
EST 323 / CSE 524: CG-HCI Graphics and Image Processing Basics Klaus Mueller Computer Science Department Stony Brook University Julian Beever Optical Illusion: Sidewalk Art Julian Beever Optical Illusion:
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 informationSampling and reconstruction
Sampling and reconstruction Week 10 Acknowledgement: The course slides are adapted from the slides prepared by Steve Marschner of Cornell University 1 Sampled representations How to store and compute with
More informationCS534 Introduction to Computer Vision. Linear Filters. Ahmed Elgammal Dept. of Computer Science Rutgers University
CS534 Introduction to Computer Vision Linear Filters Ahmed Elgammal Dept. of Computer Science Rutgers University Outlines What are Filters Linear Filters Convolution operation Properties of Linear Filters
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 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 informationSampling 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 informationSampling 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 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 informationFourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase
Fourier Transform Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2 1 3 3 3 1 sin 3 3 1 3 sin 3 1 sin 5 5 1 3 sin
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 informationFilters. 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!"!#"#$% 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 informationImage Enhancement. DD2423 Image Analysis and Computer Vision. Computational Vision and Active Perception School of Computer Science and Communication
Image Enhancement DD2423 Image Analysis and Computer Vision Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 15, 2013 Mårten Björkman (CVAP)
More informationAntialiasing 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 informationLecture 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 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 informationMatlab (see Homework 1: Intro to Matlab) Linear Filters (Reading: 7.1, ) Correlation. Convolution. Linear Filtering (warm-up slide) R ij
Matlab (see Homework : Intro to Matlab) Starting Matlab from Unix: matlab & OR matlab nodisplay Image representations in Matlab: Unsigned 8bit values (when first read) Values in range [, 255], = black,
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 informationDigital Image Processing. Digital Image Fundamentals II 12 th June, 2017
Digital Image Processing Digital Image Fundamentals II 12 th June, 2017 Image Enhancement Image Enhancement Types of Image Enhancement Operations Neighborhood Operations on Images Spatial Filtering Filtering
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 informationImage Processing. Adrien Treuille
Image Processing http://croftonacupuncture.com/db5/00415/croftonacupuncture.com/_uimages/bigstockphoto_three_girl_friends_celebrating_212140.jpg Adrien Treuille Overview Image Types Pixel Filters Neighborhood
More informationImage Processing Computer Graphics I Lecture 20. Display Color Models Filters Dithering Image Compression
15-462 Computer Graphics I Lecture 2 Image Processing April 18, 22 Frank Pfenning Carnegie Mellon University http://www.cs.cmu.edu/~fp/courses/graphics/ Display Color Models Filters Dithering Image Compression
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 informationAnnouncements. Image Processing. What s an image? Images as functions. Image processing. What s a digital image?
Image Processing Images by Pawan Sinha Today s readings Forsyth & Ponce, chapters 8.-8. http://www.cs.washington.edu/education/courses/49cv/wi/readings/book-7-revised-a-indx.pdf For Monday Watt,.3-.4 (handout)
More informationEvaluation of Visual Cryptography Halftoning Algorithms
Evaluation of Visual Cryptography Halftoning Algorithms Shital B Patel 1, Dr. Vinod L Desai 2 1 Research Scholar, RK University, Kasturbadham, Rajkot, India. 2 Assistant Professor, Department of Computer
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 informationImage and Video Processing
Image and Video Processing () Image Representation Dr. Miles Hansard miles.hansard@qmul.ac.uk Segmentation 2 Today s agenda Digital image representation Sampling Quantization Sub-sampling Pixel interpolation
More informationSampling 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 informationComputer Vision, Lecture 3
Computer Vision, Lecture 3 Professor Hager http://www.cs.jhu.edu/~hager /4/200 CS 46, Copyright G.D. Hager Outline for Today Image noise Filtering by Convolution Properties of Convolution /4/200 CS 46,
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 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 informationC. A. Bouman: Digital Image Processing - January 9, Digital Halftoning
C. A. Bouman: Digital Image Processing - January 9, 2017 1 Digital Halftoning Many image rendering technologies only have binary output. For example, printers can either fire a dot or not. Halftoning is
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 informationimage Scanner, digital camera, media, brushes,
118 Also known as rasterr graphics Record a value for every pixel in the image Often created from an external source Scanner, digital camera, Painting P i programs allow direct creation of images with
More informationFig 1: Error Diffusion halftoning method
Volume 3, Issue 6, June 013 ISSN: 77 18X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com An Approach to Digital
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 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 informationError Diffusion and Delta-Sigma Modulation for Digital Image Halftoning
Error Diffusion and Delta-Sigma Modulation for Digital Image Halftoning Thomas D. Kite, Brian L. Evans, and Alan C. Bovik Department of Electrical and Computer Engineering The University of Texas at Austin
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 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 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 informationPractical Image and Video Processing Using MATLAB
Practical Image and Video Processing Using MATLAB Chapter 10 Neighborhood processing What will we learn? What is neighborhood processing and how does it differ from point processing? What is convolution
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 Manipulation: Filters and Convolutions
Dr. Sarah Abraham University of Texas at Austin Computer Science Department Image Manipulation: Filters and Convolutions Elements of Graphics CS324e Fall 2017 Student Presentation Per-Pixel Manipulation
More informationImage Processing (EA C443)
Image Processing (EA C443) OBJECTIVES: To study components of the Image (Digital Image) To Know how the image quality can be improved How efficiently the image data can be stored and transmitted How the
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 informationComputer Graphics. Si Lu. Fall er_graphics.htm 10/02/2015
Computer Graphics Si Lu Fall 2017 http://www.cs.pdx.edu/~lusi/cs447/cs447_547_comput er_graphics.htm 10/02/2015 1 Announcements Free Textbook: Linear Algebra By Jim Hefferon http://joshua.smcvt.edu/linalg.html/
More informationOn Filter Techniques for Generating Blue Noise Mask
On Filter Techniques for Generating Blue Noise Mask Kevin J. Parker and Qing Yu Dept. of Electrical Engineering, University of Rochester, Rochester, New York Meng Yao, Color Print and Image Division Tektronix
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 informationOn Filter Techniques for Generating Blue Noise Mask
On Filter Techniques for Generating Blue Noise Mask Kevin J. Parker and Qing Yu Dept. of Electrical Engineering, University of Rochester, New York Meng Yao, Color Print and Image Division Tektronix Inc.,
More informationImage analysis. CS/CME/BIOPHYS/BMI 279 Fall 2015 Ron Dror
Image analysis CS/CME/BIOPHYS/BMI 279 Fall 2015 Ron Dror A two- dimensional image can be described as a function of two variables f(x,y). For a grayscale image, the value of f(x,y) specifies the brightness
More informationAn Improved Fast Color Halftone Image Data Compression Algorithm
International Journal of Engineering Science Invention (IJESI) ISSN (Online): 2319 6734, ISSN (Print): 2319 6726 www.ijesi.org PP. 65-69 An Improved Fast Color Halftone Image Data Compression Algorithm
More informationCSE 564: Scientific Visualization
CSE 564: Scientific Visualization Lecture 5: Image Processing Klaus Mueller Stony Brook University Computer Science Department Klaus Mueller, Stony Brook 2003 Image Processing Definitions Purpose: - enhance
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 informationAchim J. Lilienthal Mobile Robotics and Olfaction Lab, AASS, Örebro University
Achim J. Lilienthal Mobile Robotics and Olfaction Lab, Room T29, Mo, -2 o'clock AASS, Örebro University (please drop me an email in advance) achim.lilienthal@oru.se 4.!!!!!!!!! Pre-Class Reading!!!!!!!!!
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 informationIMAGES AND COLOR. N. C. State University. CSC557 Multimedia Computing and Networking. Fall Lecture # 10
IMAGES AND COLOR N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 10 IMAGES AND COLOR N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture
More informationLecture 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 informationMULTIMEDIA SYSTEMS
1 Department of Computer Engineering, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang 01076531 MULTIMEDIA SYSTEMS Pk Pakorn Watanachaturaporn, Wt ht Ph.D. PhD pakorn@live.kmitl.ac.th,
More informationFiltering. Image Enhancement Spatial and Frequency Based
Filtering Image Enhancement Spatial and Frequency Based Brent M. Dingle, Ph.D. 2015 Game Design and Development Program Mathematics, Statistics and Computer Science University of Wisconsin - Stout Lecture
More informationMultimedia Systems Giorgio Leonardi A.A Lectures 14-16: Raster images processing and filters
Multimedia Systems Giorgio Leonardi A.A.2014-2015 Lectures 14-16: Raster images processing and filters Outline (of the following lectures) Light and color processing/correction Convolution filters: blurring,
More informationA.V.C. COLLEGE OF ENGINEERING DEPARTEMENT OF CSE CP7004- IMAGE PROCESSING AND ANALYSIS UNIT 1- QUESTION BANK
A.V.C. COLLEGE OF ENGINEERING DEPARTEMENT OF CSE CP7004- IMAGE PROCESSING AND ANALYSIS UNIT 1- QUESTION BANK STAFF NAME: TAMILSELVAN K UNIT I SPATIAL DOMAIN PROCESSING Introduction to image processing
More informationMonochrome Image Reproduction
Monochrome Image Reproduction 1995-2016 Josef Pelikán & Alexander Wilkie CGG MFF UK Praha pepca@cgg.mff.cuni.cz http://cgg.mff.cuni.cz/~pepca/ 1 / 27 Preception of Grey Grey has a single attribute intensity
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 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 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 informationMidterm 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 informationSampling 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 informationCSCI 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 informationImages and Filters. EE/CSE 576 Linda Shapiro
Images and Filters EE/CSE 576 Linda Shapiro What is an image? 2 3 . We sample the image to get a discrete set of pixels with quantized values. 2. For a gray tone image there is one band F(r,c), with values
More informationMethods for Generating Blue-Noise Dither Matrices for Digital Halftoning
Methods for Generating Blue-Noise Dither Matrices for Digital Halftoning Kevin E. Spaulding, Rodney L. Miller and Jay Schildkraut Eastman Kodak Company Imaging Research and Advanced Development, Rochester,
More informationFourier Transforms in Radio Astronomy
Fourier Transforms in Radio Astronomy Kavilan Moodley, UKZN Slides taken from N Gupta s lectures: SKA School 2013 van-cittert Zernike theorem Extended, quasi-monochromatic, incoherent source X (l,m) Y
More informationLAB MANUAL SUBJECT: IMAGE PROCESSING BE (COMPUTER) SEM VII
LAB MANUAL SUBJECT: IMAGE PROCESSING BE (COMPUTER) SEM VII IMAGE PROCESSING INDEX CLASS: B.E(COMPUTER) SR. NO SEMESTER:VII TITLE OF THE EXPERIMENT. 1 Point processing in spatial domain a. Negation of an
More informationDigital Image Fundamentals and Image Enhancement in the Spatial Domain
Digital Image Fundamentals and Image Enhancement in the Spatial Domain Mohamed N. Ahmed, Ph.D. Introduction An image may be defined as 2D function f(x,y), where x and y are spatial coordinates. The amplitude
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 Enhancement in the Spatial Domain
Image Enhancement in the Spatial Domain Algorithms for improving the visual appearance of images Gamma correction Contrast improvements Histogram equalization Noise reduction Image sharpening Optimality
More informationVirtual Restoration of old photographic prints. Prof. Filippo Stanco
Virtual Restoration of old photographic prints Prof. Filippo Stanco Many photographic prints of commercial / historical value are being converted into digital form. This allows: Easy ubiquitous fruition:
More informationImage Enhancement using Histogram Equalization and Spatial Filtering
Image Enhancement using Histogram Equalization and Spatial Filtering Fari Muhammad Abubakar 1 1 Department of Electronics Engineering Tianjin University of Technology and Education (TUTE) Tianjin, P.R.
More informationError Diffusion without Contouring Effect
Error Diffusion without Contouring Effect Wei-Yu Han and Ja-Chen Lin National Chiao Tung University, Department of Computer and Information Science Hsinchu, Taiwan 3000 Abstract A modified error-diffusion
More informationAppendix B. Design Implementation Description For The Digital Frequency Demodulator
Appendix B Design Implementation Description For The Digital Frequency Demodulator The DFD design implementation is divided into four sections: 1. Analog front end to signal condition and digitize the
More informationImage processing for gesture recognition: from theory to practice. Michela Goffredo University Roma TRE
Image processing for gesture recognition: from theory to practice 2 Michela Goffredo University Roma TRE goffredo@uniroma3.it Image processing At this point we have all of the basics at our disposal. We
More informationA New Metric for Color Halftone Visibility
A New Metric for Color Halftone Visibility Qing Yu and Kevin J. Parker, Robert Buckley* and Victor Klassen* Dept. of Electrical Engineering, University of Rochester, Rochester, NY *Corporate Research &
More informationDigital Halftoning. Sasan Gooran. PhD Course May 2013
Digital Halftoning Sasan Gooran PhD Course May 2013 DIGITAL IMAGES (pixel based) Scanning Photo Digital image ppi (pixels per inch): Number of samples per inch ppi (pixels per inch) ppi (scanning resolution):
More informationDIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 2002
DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 22 Topics: Human eye Visual phenomena Simple image model Image enhancement Point processes Histogram Lookup tables Contrast compression and stretching
More informationStudy guide for Graduate Computer Vision
Study guide for Graduate Computer Vision Erik G. Learned-Miller Department of Computer Science University of Massachusetts, Amherst Amherst, MA 01003 November 23, 2011 Abstract 1 1. Know Bayes rule. What
More informationMULTIMEDIA SYSTEMS
1 Department of Computer Engineering, g, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang 01076531 MULTIMEDIA SYSTEMS Pakorn Watanachaturaporn, Ph.D. pakorn@live.kmitl.ac.th, pwatanac@gmail.com
More information