ECE 484 Digital Image Processing Lec 12 - Mid Term Review
|
|
- Richard O’Neal’
- 5 years ago
- Views:
Transcription
1 ECE 484 Digital Image Processing Lec 12 - Mid Term Review Zhu Li Dept of CSEE, UMKC Office: FH560E, lizhu@umkc.edu, Ph: x slides created with WPS Office Linux and EqualX equation editor Z. Li, ECE 484 Digital Image Processing, 2018 p.1
2 Outline About Mid-Term Review of the coverage so far Image Formation - Geometry and Color Point Operation Linear Filters Transform Domain Filters Non-Linear Filters Summary Z. Li, ECE 484 Digital Image Processing, 2018 p.2
3 DIP Mid-term Date: 10/25 (Thursday), in class Close book, no cell phone, but allow 1 A4/letter sized handwritten cheating sheet Mid-term coverage: Image Formation: Geometry constraint Image Formation: Color space and features Point operation: histogram equalization, quantization Linear Filtering Non-Linear Filtering: median, bilateral, cross bilateral, and guided filtering Freq domain filtering Image Resampling Image Restoration Green Cyan (120 o ) (180 o ) Blue (240 o ) White Black Val ue Yellow (60 o ) Red Magenta (0 o ) (300 o ) Hue Z. Li, ECE 484 Digital Image Processing, 2018 p.3
4 Image Formation - Geometry Projection Model Homography Z. Li, ECE 484 Digital Image Processing, 2018 p.4 ú ú ú ú û ù ê ê ê ê ë é ú ú ú û ù ê ê ê ë é ú ú ú û ù ê ê ê ë é = ú ú ú û ù ê ê ê ë é z y x t r r r t r r r t r r r v u v u w z y x b a
5 Image Wrapping VL_FEAT has an implementation: zero singular value V column Z. Li, Digital Image Processing, 2018 p.5
6 HSV Color Model Hue, Satuation and Value (brightness) color model A cone with inituitive appeal of painters' tint, shade and tone model pure red: H=0, S=1, V=1. tints: adding white pigments, decreasing saturation shades: adding black, decrease brightness tones: dereasing S and V Human can differentiate approx. 128 hues, and 130 levels of saturation The number of values (brightness) is color dependent, approx 16 for blue, and 23 for yellow Z. Li, Digital Image Processing, 2018 p.6
7 MPEG-7 Scalable Color Descriptor Scalable Color Descriptor: Scalable Color Descriptor (SCD) is in the form of a color histogram in the HSV color space encoded using a Haar transform. H is quantized to 16 bin and S and V are quantized to 4 bins each, total 256 bins. The pixel count for each bin is quantized to 4 bits, so at max 256x4=1024 bits for representing. The distance between two images are therefore hamming distance, Scalability thru Haar trans. Green (120 o ) Value Cyan (180 o ) Blue (240 o ) Yellow (60 o ) Magenta (300 o ) Red (0 o ) White Hue Black Saturation Z. Li, Digital Image Processing, Fall 2018 p.7
8 Gamma Correction - Adjust dynamic ranges Matching Display characteristics power-law response functions in practice CRT Intensity-to-voltage function has ¼ 1.8~2.5 Camera capturing distortion with c = Similar device curves in scanners, printers, power-law transformations are also useful for general purpose contrast manipulation Z. Li, ECE 484 Digital Image Processing, 2018 p.8
9 Histogram Equalization Objectives goal: map the each luminance level to a new value such that the output image has approximately uniform distribution of gray levels two desired properties monotonic (non-decreasing) function: no value reversals [0,1] [0.1] : the output range being the same as the input range pdf cdf 1 1 o 1 o 1 Z. Li, ECE 484 Digital Image Processing, 2018 p.9
10 Re-Cap: Linear Filters Convolution * 1/9 1/9 1 1/9 1 1/9 1 1/9 1 1/9 1 1/9 1 1/9 1 1/ = Padding Complexity: M*N*K 2 Z. Li, Digital Image Processing, 2018 p.10
11 Properties of Linear Filtering Convolution properties Shift-Invariant: f(m-k, n-j)*h = g(m-k, n-j), if f*h=g Associative: f*h 1 *h 2 = f*(h 1 *h 2 ) this can save a lot of complexity Distributive: f*h1 + f*h2 = f*(h1+h2) useful in SIFT s DoG filtering. Applications Scale space filtering with successive Gaussian DoG filtering with difference of Gaussian blurred images * = Z. Li, Digital Image Processing, 2018 p.11
12 Image Smoothing, Edge Detection Smoothing kernels from Gaussian im=imread('data/lenna.png'); im=rgb2gray(im); im=imresize(im, 0.5); sigma = 1.6.*sqrt([1:6]); m=15*ones(1,6); for k=1:length(sigma) h{k} = fspecial('gaussian', m(k), sigma(k)); if k>=2 h_dog{k-1} = h{k}-h{k-1}; im_dog{k-1} = imfilter(im, h{k}-h{k-1}); imagesc(im_dog{k-1}); axis off; title(sprintf('dog %s = %1.2f-%1.2f', '\sigma', sigma(k),sigma(k-1))); else imagesc(im); title('dog \sigma = 1'); axis off; end end fprintf('\n...end image smoothing'); Z. Li, Digital Image Processing, 2018 p.12
13 Sharpening Adding edge details back %sharpening for k=1:length(sigma) %sharpen by adding edge info im_sharp{k} = im + 0.5*im_dog{k}; figure(24); colormap('gray'); subplot(2,3,k); imagesc(im_sharp{k}); title(sprintf('sharpen: %s=%1.2f', '\sigma', sigma(k))); axis off; end Z. Li, Digital Image Processing, 2018 p.13
14 Bilateral Filter Filtering process * W c W s W s *W c input Output geometry kernelphotometry kernel bilateral kernel Z. Li, Digital Image Processing, 2018 p.14
15 Bilateral Filter No averaging across edges Z. Li, Digital Image Processing, 2018 p.15
16 BF Color Images Just need to adapt the range/photometry kernel works for hyperspectral images as well Z. Li, Digital Image Processing, 2018 p.16
17 Cross Bilateral/Guided Filters Cross Bilateral Filtering CBF: use a different image to derive range kernel Guided Filter solving local linear regression for a linear filter model from guided image smooth if input image and guide image are not correlated (a k is small) Edge preserving if a k is big. Z. Li, Digital Image Processing, 2018 p.17
18 Denoising with multiple exposure images Guided fitler for image denoising A = imread('toysnoflash.png'); G = imread('toysflash.png'); r = 3; s = 0.001*diff(getrangefromclass(G)).^2; B = imguidedfilter(a, G, 'NeighborhoodSize', r, 'DegreeOfSmoothing',s); Z. Li, Digital Image Processing, 2018 p.18
19 Freq Domain Filtering: 2-D FT illustrated FFT2 illustrated: real-valued real imag Z. Li, Digital Image Processing, 2018 p.19
20 notes about 2D-DFT Output of the Fourier transform is a complex number Decompose the complex number as the magnitude and phase components In Matlab: u = real(z), v = imag(z), r = abs(z), and theta = angle(z) real function Z. Li, Digital Image Processing, 2018 p.20
21 Filtering in Frequency Domain Why filtering in Freq Domain? faster convolution, if involves large kernels better denoising (notch, inverse and wiener filtering) Z. Li, Digital Image Processing, 2018 p.21
22 Sampling in Time Domain Computer needs a discrete representation of signals Many signals originate as continuous-time signals, e.g. conventional music or voice By sampling a continuous-time signal at isolated, equally-spaced points in time, we obtain a sequence of numbers s [ n] = s( ) n T s n {, -2, -1, 0, 1, 2, } T s is the sampling period. s ( t) = s( t) d ( t - n ) å sampled T s n= - impulse train s sampled Sampled analog waveform Z. Li, Digital Image Processing, 2018 p.22 T s ( t) T s t s(t)
23 Consequence in Freq Domain Multiplication with sampling train function, is convolving in freq domain Replicates spectrum of continuous-time signal At offsets that are integer multiples of sampling frequency Fourier series of impulse train where s = 2 f s ( ) = å dt s t d ( t - n Ts ) = + cos( s t) + cos(2 s t) +... n= - Ts Ts Ts 1 g( t) = f ( t) dt s ( t) = f ( t) + 2 f ( t)cos( s t) + 2 f ( t)cos(2 s t) T Example F( ) s ( +... ) Modulation by cos( s t) G( ) Modulation by cos(2 s t) -2 f max 2 f max - s - s s s gap if and only if 2 f max < 2 f s - 2 f max Û fs > 2 f max Z. Li, Digital Image Processing, 2018 p.23
24 Sampling in 2D Very similar Z. Li, Digital Image Processing, 2018 p.24
25 Filtering to combat Aliasing Pre-filtering to limit image bandwidth to fit in sampling rate Z. Li, Digital Image Processing, 2018 p.25
26 Resampling Interpolation 2D interpolation Z. Li, Digital Image Processing, 2018 p.26
27 Bilinear & DCTIF Interpolation Bilinear DCTIF Z. Li, Digital Image Processing, 2018 p.27
28 Image Restoration Image Restoration from Degradation Degradation sources: Noise - independent of (x,y) Point Spread Function (PSF) - a function of (x, y), and assuming linear Z. Li, ECE 484 Digital Image Processing, 2018 p.28
29 Noise Supression Spatial Filtering Linear: Mean, Gaussian, and Media Filters Non-Linear: Bilateral Filters/Guided Filters Freq Domain Filtering Low Pass Filters Band pass Filters Notch filters for repetive patterns Z. Li, ECE 484 Digital Image Processing, 2018 p.29
30 Inverse Filtering Degradation from PSF Z. Li, ECE 484 Digital Image Processing, 2018 p.30 ), ( ), ( ), ( ), ( ), ( ), ( ˆ v u H v u N v u F v u H v u G v u F + = =
31 Noise Magnifying Problem with Inverse filters G(u,v)=F(u,v)H(u,v)+N(u,v) Unknown noise => ˆ G( u, v) F ( u, v) = = F( u, v) + H ( u, v) N( u, v) H ( u, v) Estimate of original image Problem: 0 or small values Sol: limit the frequency around the origin Z. Li, ECE 484 Digital Image Processing, 2018 p.31
32 The Inverse Filtering - Cut offs and Pseudo Inv Inverse filter with cut-off: Pseudo-inverse filter: Can the filter take values between 1/H(u,v) and zero? Can we model noise directly? 32
33 Wiener filter goal: restoration with minimum mean-square error (MSE) optimal solution (nonlinear): restrict to linear space-invariant filter find optimal linear filter W(u,v) with min. MSE 33
34 Wiener filter Min MSE Fitlering: goal: restoration with minimum mean-square error (MSE) find optimal linear filter W(u,v) with min. MSE orthogonal condition correlation function wide-sense-stationary (WSS) signals Fourier Transform: from correlation to spectrum 34
35 observations about Wiener filter If no noise, S 0, it is a Pseudo Inv Filter: Pseudo inverse filter If no blur, H(u,v)=1 (Wiener smoothing filter) More suppression on noisier frequency bands 35
36 Wiener Filtering Wiener Filtering Solving for a MSE objective function, that has freq domain solution Basically inverse fitler but reflect the Signal to Noise ratios at freq locations Applications in debluring, and motion debluring Z. Li, ECE 484 Digital Image Processing, 2018 p.36
37 Summary Relax, mid-term is more for me to check on the coverage effectiveness, will adjust accordingly. Focus on your homework programming assignments, that is more useful Start thinking a course project that leads to short conf paper (4 page) submission that will give you 25% extra credit. Cheating sheet: sampling theorem, quantization,...etc. Z. Li, ECE 484 Digital Image Processing, 2018 p.37
EECS 556 Image Processing W 09. Image enhancement. Smoothing and noise removal Sharpening filters
EECS 556 Image Processing W 09 Image enhancement Smoothing and noise removal Sharpening filters What is image processing? Image processing is the application of 2D signal processing methods to images Image
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 informationIT Digital Image ProcessingVII Semester - Question Bank
UNIT I DIGITAL IMAGE FUNDAMENTALS PART A Elements of Digital Image processing (DIP) systems 1. What is a pixel? 2. Define Digital Image 3. What are the steps involved in DIP? 4. List the categories of
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 informationImage Analysis & Retrieval Lec 12 - Mid-Term Review
CS/EE 5590 / ENG 401 Special Topics, Spring 2018 Image Analysis & Retrieval Lec 12 - Mid-Term Review Zhu Li Dept of CSEE, UMKC http://l.web.umkc.edu/lizhu Office Hour: Tue/Thr 2:30-4pm@FH560E, Contact:
More informationAnno accademico 2006/2007. Davide Migliore
Robotica Anno accademico 6/7 Davide Migliore migliore@elet.polimi.it Today What is a feature? Some useful information The world of features: Detectors Edges detection Corners/Points detection Descriptors?!?!?
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 informationDigital Image Processing. Image Enhancement in the Frequency Domain
Digital Image Processing Image Enhancement in the Frequency Domain Topics Frequency Domain Enhancements Fourier Transform Convolution High Pass Filtering in Frequency Domain Low Pass Filtering in Frequency
More informationEE795: Computer Vision and Intelligent Systems
EE795: Computer Vision and Intelligent Systems Spring 2012 TTh 17:30-18:45 WRI C225 Lecture 04 130131 http://www.ee.unlv.edu/~b1morris/ecg795/ 2 Outline Review Histogram Equalization Image Filtering Linear
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 informationEEM 463 Introduction to Image Processing. Week 3: Intensity Transformations
EEM 463 Introduction to Image Processing Week 3: Intensity Transformations Fall 2013 Instructor: Hatice Çınar Akakın, Ph.D. haticecinarakakin@anadolu.edu.tr Anadolu University Enhancement Domains Spatial
More informationDigital Image Fundamentals
Digital Image Fundamentals Image Quality Objective/ subjective Machine/human beings Mathematical and Probabilistic/ human intuition and perception 6 Structure of the Human Eye photoreceptor cells 75~50
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 informationDigital Image Processing
Digital Image Processing Third Edition Rafael C. Gonzalez University of Tennessee Richard E. Woods MedData Interactive PEARSON Prentice Hall Pearson Education International Contents Preface xv Acknowledgments
More informationImage Enhancement. Digital Image Processing, Pratt Chapter 10 (pages ) Part 1: pixel-based operations
Image Enhancement Digital Image Processing, Pratt Chapter 10 (pages 243-261) Part 1: pixel-based operations Image Processing Algorithms Spatial domain Operations are performed in the image domain Image
More informationScaled representations
Scaled representations Big bars (resp. spots, hands, etc.) and little bars are both interesting Stripes and hairs, say Inefficient to detect big bars with big filters And there is superfluous detail in
More informationImage restoration. Lecture 14. Milan Gavrilovic Centre for Image Analysis Uppsala University
Image restoration Lecture 14 Milan Gavrilovic milan@cb.uu.se Centre for Image Analysis Uppsala University Computer Assisted Image Analysis 2009-05-08 M. Gavrilovic (Uppsala University) L14 Image restoration
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 2 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationAn Introduc+on to Mathema+cal Image Processing IAS, Park City Mathema2cs Ins2tute, Utah Undergraduate Summer School 2010
An Introduc+on to Mathema+cal Image Processing IAS, Park City Mathema2cs Ins2tute, Utah Undergraduate Summer School 2010 Luminita Vese Todd WiCman Department of Mathema2cs, UCLA lvese@math.ucla.edu wicman@math.ucla.edu
More informationComputer Vision I. Announcements. Fourier Tansform. Efficient Implementation. Edge and Corner Detection. CSE252A Lecture 13.
Announcements Edge and Corner Detection HW3 assigned CSE252A Lecture 13 Efficient Implementation Both, the Box filter and the Gaussian filter are separable: First convolve each row of input image I with
More informationCS4442/9542b Artificial Intelligence II prof. Olga Veksler
CS4442/9542b Artificial Intelligence II prof. Olga Veksler Lecture 8 Computer Vision Introduction, Filtering Some slides from: D. Jacobs, D. Lowe, S. Seitz, A.Efros, X. Li, R. Fergus, J. Hayes, S. Lazebnik,
More informationFundamentals of Digital Image Processing
\L\.6 Gw.i Fundamentals of Digital Image Processing A Practical Approach with Examples in Matlab Chris Solomon School of Physical Sciences, University of Kent, Canterbury, UK Toby Breckon School of Engineering,
More informationNoise Model. Important Noise Probability Density Functions (Cont.) Important Noise Probability Density Functions
Others -- Noise Removal Techniques -- Edge Detection Techniques -- Geometric Operations -- Color Image Processing -- Color Spaces Xiaojun Qi Noise Model The principal sources of noise in digital images
More informationDigital Image Processing COSC 6380/4393
Digital Image Processing COSC 6380/4393 Lecture 21 Nov 16 th, 2017 Pranav Mantini Ack: Shah. M Image Processing Geometric Transformation Point Operations Filtering (spatial, Frequency) Input Restoration/
More informationComputer Vision 2. SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung. Computer Vision 2 Dr. Benjamin Guthier
Computer Vision 2 SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung Computer Vision 2 Dr. Benjamin Guthier 1. IMAGE PROCESSING Computer Vision 2 Dr. Benjamin Guthier Content of this Chapter Non-linear
More informationECE 484 Digital Image Processing Lec 17 - Part II Review & Final Projects Topics
ECE 484 Digital Image Processing Lec 17 - Part II Review & Final Projects opics Zhu Li Dept of CSEE, UMKC Office: FH560E, Email: lizhu@umkc.edu, Ph: x 2346. http://l.web.umkc.edu/lizhu slides created with
More informationSampling and Reconstruction
Sampling and Reconstruction Sampling and Reconstruction Sampling and Spatial Resolution Spatial Aliasing Problem: Spatial aliasing is insufficient sampling of data along the space axis, which occurs because
More informationImage Processing. Filtering. Slide 1
Image Processing Filtering Slide 1 Preliminary Image generation Original Noise Image restoration Result Slide 2 Preliminary Classic application: denoising However: Denoising is much more than a simple
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 informationImage Processing Fundamentals. Nicolas Vazquez Principal Software Engineer National Instruments
Image Processing Fundamentals Nicolas Vazquez Principal Software Engineer National Instruments Agenda Objectives and Motivations Enhancing Images Checking for Presence Locating Parts Measuring Features
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 informationPop Quiz 1 [10 mins]
Pop Quiz 1 [10 mins] 1. An audio signal makes 250 cycles in its span (or has a frequency of 250Hz). How many samples do you need, at a minimum, to sample it correctly? [1] 2. If the number of bits is reduced,
More informationComputer Vision I - Basics of Image Processing Part 1
Computer Vision I - Basics of Image Processing Part 1 Carsten Rother 28/10/2014 Computer Vision I: Basics of Image Processing Link to lectures Computer Vision I: Basics of Image Processing 28/10/2014 2
More informationLecture 12 Color model and color image processing
Lecture 12 Color model and color image processing Color fundamentals Color models Pseudo color image Full color image processing Color fundamental The color that humans perceived in an object are determined
More informationC2: Medical Image Processing Linwei Wang
C2: Medical Image Processing 4005-759 Linwei Wang Content Enhancement Improve visual quality of the image When the image is too dark, too light, or has low contrast Highlight certain features of the image
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 informationCSci 4968 and 6270 Computational Vision, Fall Semester, Lectures 2&3, Image Processing
CSci 4968 and 6270 Computational Vision, Fall Semester, 2010-2011 Lectures 2&3, Image Processing 1 Introduction Goals of SIFT Dense, repeatable, matchable features Invariance to scale and rotation Pseudo-invariance
More informationPolytechnic Institute of NYU Fall 2012 EL5123/BE DIGITAL IMAGE PROCESSING
Polytechnic Institute of NYU Fall EL53/BE63 --- DIGITAL IMAGE PROCESSING Yao Wang Midterm Exam (/4, 3:-5:3PM) Closed book, sheet of notes (double sided) allowed. No peeking into neighbors or unauthorized
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 informationImage Enhancement: To improve the quality of images
Image Enhancement: To improve the quality of images Examples: Noise reduction (to improve SNR or subjective quality) Change contrast, brightness, color etc. Image smoothing Image sharpening Modify image
More informationLast update: May 4, Vision. CMSC 421: Chapter 24. CMSC 421: Chapter 24 1
Last update: May 4, 200 Vision CMSC 42: Chapter 24 CMSC 42: Chapter 24 Outline Perception generally Image formation Early vision 2D D Object recognition CMSC 42: Chapter 24 2 Perception generally Stimulus
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 informationME/CS 132: Introduction to Vision-based Robot Navigation! Low-level Image Processing" Larry Matthies"
ME/CS 132: Introduction to Vision-based Robot Navigation! Low-level Image Processing" Larry Matthies" lhm@jpl.nasa.gov, 818-354-3722" Announcements" First homework grading is done! Second homework is due
More informationWhat will we learn? Neighborhood processing. Convolution and correlation. Neighborhood processing. Chapter 10 Neighborhood Processing
What will we learn? Lecture Slides ME 4060 Machine Vision and Vision-based Control Chapter 10 Neighborhood Processing By Dr. Debao Zhou 1 What is neighborhood processing and how does it differ from point
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 informationExamination in Image Processing
Umeå University, TFE Ulrik Söderström 203-03-27 Examination in Image Processing Time for examination: 4.00 20.00 Please try to extend the answers as much as possible. Do not answer in a single sentence.
More informationDigital Image Processing
Digital Image Processing Image Restoration and Reconstruction (Noise Removal) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Image Restoration
More informationBiometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)
Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html
More informationProf. Feng Liu. Winter /15/2019
Prof. Feng Liu Winter 2019 http://www.cs.pdx.edu/~fliu/courses/cs410/ 01/15/2019 Last Time Filter 2 Today More on Filter Feature Detection 3 Filter Re-cap noisy image naïve denoising Gaussian blur better
More informationThe 2D Fourier transform & image filtering
Luleå University of Technology Matthew Thurley and Johan Carlson Last revision: Oct 27, 2011 Industrial Image Analysis E0005E Product Development Phase 6 The 2D Fourier transform & image filtering Contents
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 information2.161 Signal Processing: Continuous and Discrete Fall 2008
MIT OpenCourseWare http://ocw.mit.edu 2.161 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. MASSACHUSETTS
More informationIntensity Transformations and Spatial Filtering
77 Chapter 3 Intensity Transformations and Spatial Filtering Spatial domain refers to the image plane itself, and image processing methods in this category are based on direct manipulation of pixels in
More informationReview of Filtering. Filtering in frequency domain
Review of Filtering Filtering in frequency domain Can be faster than filtering in spatial domain (for large filters) Can help understand effect of filter Algorithm: 1. Convert image and filter to fft (fft2
More informationconvolution shift invariant linear system Fourier Transform Aliasing and sampling scale representation edge detection corner detection
COS 429: COMPUTER VISON Linear Filters and Edge Detection convolution shift invariant linear system Fourier Transform Aliasing and sampling scale representation edge detection corner detection Reading:
More informationBabu Madhav Institute of Information Technology Years Integrated M.Sc.(IT)(Semester - 7)
5 Years Integrated M.Sc.(IT)(Semester - 7) 060010707 Digital Image Processing UNIT 1 Introduction to Image Processing Q: 1 Answer in short. 1. What is digital image? 1. Define pixel or picture element?
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 informationPSD2B Digital Image Processing. Unit I -V
PSD2B Digital Image Processing Unit I -V Syllabus- Unit 1 Introduction Steps in Image Processing Image Acquisition Representation Sampling & Quantization Relationship between pixels Color Models Basics
More informationDigital Image Processing, 2nd ed. Digital Image Processing, 2nd ed. The principal objective of enhancement
Chapter 3 Image Enhancement in the Spatial Domain The principal objective of enhancement to process an image so that the result is more suitable than the original image for a specific application. Enhancement
More informationImage Processing. Traitement d images. Yuliya Tarabalka Tel.
Traitement d images Yuliya Tarabalka yuliya.tarabalka@hyperinet.eu yuliya.tarabalka@gipsa-lab.grenoble-inp.fr Tel. 04 76 82 62 68 Noise reduction Image restoration Restoration attempts to reconstruct an
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) Gradients and edges edges are points of large gradient magnitude edge detection strategy 1. determine
More informationFiltering Images in the Spatial Domain Chapter 3b G&W. Ross Whitaker (modified by Guido Gerig) School of Computing University of Utah
Filtering Images in the Spatial Domain Chapter 3b G&W Ross Whitaker (modified by Guido Gerig) School of Computing University of Utah 1 Overview Correlation and convolution Linear filtering Smoothing, kernels,
More informationJNTUWORLD. 4. Prove that the average value of laplacian of the equation 2 h = ((r2 σ 2 )/σ 4 ))exp( r 2 /2σ 2 ) is zero. [16]
Code No: 07A70401 R07 Set No. 2 1. (a) What are the basic properties of frequency domain with respect to the image processing. (b) Define the terms: i. Impulse function of strength a ii. Impulse function
More informationImage gradients and edges April 10 th, 2018
Image gradients and edges April th, 28 Yong Jae Lee UC Davis PS due this Friday Announcements Questions? 2 Last time Image formation Linear filters and convolution useful for Image smoothing, removing
More informationColor and Shading. Color. Shapiro and Stockman, Chapter 6. Color and Machine Vision. Color and Perception
Color and Shading Color Shapiro and Stockman, Chapter 6 Color is an important factor for for human perception for object and material identification, even time of day. Color perception depends upon both
More informationFeature descriptors and matching
Feature descriptors and matching Detections at multiple scales Invariance of MOPS Intensity Scale Rotation Color and Lighting Out-of-plane rotation Out-of-plane rotation Better representation than color:
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spring 2014 TTh 14:30-15:45 CBC C313 Lecture 03 Image Processing Basics 13/01/28 http://www.ee.unlv.edu/~b1morris/ecg782/
More informationComputer Vision. Fourier Transform. 20 January Copyright by NHL Hogeschool and Van de Loosdrecht Machine Vision BV All rights reserved
Van de Loosdrecht Machine Vision Computer Vision Fourier Transform 20 January 2017 Copyright 2001 2017 by NHL Hogeschool and Van de Loosdrecht Machine Vision BV All rights reserved j.van.de.loosdrecht@nhl.nl,
More informationECG782: Multidimensional Digital Signal Processing
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu ECG782: Multidimensional Digital Signal Processing Spatial Domain Filtering http://www.ee.unlv.edu/~b1morris/ecg782/ 2 Outline Background Intensity
More informationx' = c 1 x + c 2 y + c 3 xy + c 4 y' = c 5 x + c 6 y + c 7 xy + c 8
1. Explain about gray level interpolation. The distortion correction equations yield non integer values for x' and y'. Because the distorted image g is digital, its pixel values are defined only at integer
More informationCvision 3 Color and Noise
Cvision 3 Color and Noise António J. R. Neves (an@ua.pt) & João Paulo Cunha IEETA / Universidade de Aveiro Outline Color spaces Color processing Noise Acknowledgements: Most of this course is based on
More informationCSci 4968 and 6270 Computational Vision, Fall Semester, 2011 Lectures 2&3, Image Processing. Corners, boundaries, homogeneous regions, textures?
1 Introduction CSci 4968 and 6270 Computational Vision, Fall Semester, 2011 Lectures 2&3, Image Processing How Do We Start Working with Images? Corners, boundaries, homogeneous regions, textures? How do
More informationFiltering in frequency domain
Filtering in frequency domain FFT FFT = Inverse FFT Filtering in frequency domain Can be faster than filtering in spatial domain (for large filters) Can help understand effect of filter Algorithm: 1. Convert
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 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 informationLecture 4: Spatial Domain Transformations
# Lecture 4: Spatial Domain Transformations Saad J Bedros sbedros@umn.edu Reminder 2 nd Quiz on the manipulator Part is this Fri, April 7 205, :5 AM to :0 PM Open Book, Open Notes, Focus on the material
More informationCoE4TN3 Medical Image Processing
CoE4TN3 Medical Image Processing Image Restoration Noise Image sensor might produce noise because of environmental conditions or quality of sensing elements. Interference in the image transmission channel.
More informationIntroduction to Digital Image Processing
Introduction to Digital Image Processing Ranga Rodrigo June 9, 29 Outline Contents Introduction 2 Point Operations 2 Histogram Processing 5 Introduction We can process images either in spatial domain or
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 informationImage Enhancement in Spatial Domain (Chapter 3)
Image Enhancement in Spatial Domain (Chapter 3) Yun Q. Shi shi@njit.edu Fall 11 Mask/Neighborhood Processing ECE643 2 1 Point Processing ECE643 3 Image Negatives S = (L 1) - r (3.2-1) Point processing
More informationImage Deconvolution.
Image Deconvolution. Mathematics of Imaging. HW3 Jihwan Kim Abstract This homework is to implement image deconvolution methods, especially focused on a ExpectationMaximization(EM) algorithm. Most of this
More informationImage gradients and edges April 11 th, 2017
4//27 Image gradients and edges April th, 27 Yong Jae Lee UC Davis PS due this Friday Announcements Questions? 2 Last time Image formation Linear filters and convolution useful for Image smoothing, removing
More informationEXAM SOLUTIONS. Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006,
School of Computer Science and Communication, KTH Danica Kragic EXAM SOLUTIONS Image Processing and Computer Vision Course 2D1421 Monday, 13 th of March 2006, 14.00 19.00 Grade table 0-25 U 26-35 3 36-45
More informationSuper-Resolution. Many slides from Miki Elad Technion Yosi Rubner RTC and more
Super-Resolution Many slides from Mii Elad Technion Yosi Rubner RTC and more 1 Example - Video 53 images, ratio 1:4 2 Example Surveillance 40 images ratio 1:4 3 Example Enhance Mosaics 4 5 Super-Resolution
More informationChapter - 2 : IMAGE ENHANCEMENT
Chapter - : IMAGE ENHANCEMENT The principal objective of enhancement technique is to process a given image so that the result is more suitable than the original image for a specific application Image Enhancement
More informationBiomedical Image Analysis. Spatial Filtering
Biomedical Image Analysis Contents: Spatial Filtering The mechanics of Spatial Filtering Smoothing and sharpening filters BMIA 15 V. Roth & P. Cattin 1 The Mechanics of Spatial Filtering Spatial filter:
More informationPhysical Color. Color Theory - Center for Graphics and Geometric Computing, Technion 2
Color Theory Physical Color Visible energy - small portion of the electro-magnetic spectrum Pure monochromatic colors are found at wavelengths between 380nm (violet) and 780nm (red) 380 780 Color Theory
More informationImage Pyramids and Applications
Image Pyramids and Applications Computer Vision Jia-Bin Huang, Virginia Tech Golconda, René Magritte, 1953 Administrative stuffs HW 1 will be posted tonight, due 11:59 PM Sept 25 Anonymous feedback Previous
More informationImage restoration. Restoration: Enhancement:
Image restoration Most images obtained by optical, electronic, or electro-optic means is likely to be degraded. The degradation can be due to camera misfocus, relative motion between camera and object,
More informationSIFT - scale-invariant feature transform Konrad Schindler
SIFT - scale-invariant feature transform Konrad Schindler Institute of Geodesy and Photogrammetry Invariant interest points Goal match points between images with very different scale, orientation, projective
More informationImage processing. Reading. What is an image? Brian Curless CSE 457 Spring 2017
Reading Jain, Kasturi, Schunck, Machine Vision. McGraw-Hill, 1995. Sections 4.2-4.4, 4.5(intro), 4.5.5, 4.5.6, 5.1-5.4. [online handout] Image processing Brian Curless CSE 457 Spring 2017 1 2 What is an
More informationVideo Mosaics for Virtual Environments, R. Szeliski. Review by: Christopher Rasmussen
Video Mosaics for Virtual Environments, R. Szeliski Review by: Christopher Rasmussen September 19, 2002 Announcements Homework due by midnight Next homework will be assigned Tuesday, due following Tuesday.
More informationSampling, Resampling, and Warping. COS 426, Spring 2014 Tom Funkhouser
Sampling, Resampling, and Warping COS 426, Spring 2014 Tom Funkhouser Image Processing Goal: read an image, process it, write the result input.jpg output.jpg imgpro input.jpg output.jpg histogram_equalization
More informationImage Restoration and Reconstruction
Image Restoration and Reconstruction Image restoration Objective process to improve an image Recover an image by using a priori knowledge of degradation phenomenon Exemplified by removal of blur by deblurring
More informationEdge and Texture. CS 554 Computer Vision Pinar Duygulu Bilkent University
Edge and Texture CS 554 Computer Vision Pinar Duygulu Bilkent University Filters for features Previously, thinking of filtering as a way to remove or reduce noise Now, consider how filters will allow us
More informationEECS150 - Digital Design Lecture 14 FIFO 2 and SIFT. Recap and Outline
EECS150 - Digital Design Lecture 14 FIFO 2 and SIFT Oct. 15, 2013 Prof. Ronald Fearing Electrical Engineering and Computer Sciences University of California, Berkeley (slides courtesy of Prof. John Wawrzynek)
More informationVisible Color. 700 (red) 580 (yellow) 520 (green)
Color Theory Physical Color Visible energy - small portion of the electro-magnetic spectrum Pure monochromatic colors are found at wavelengths between 380nm (violet) and 780nm (red) 380 780 Color Theory
More informationFiltering and Enhancing Images
KECE471 Computer Vision Filtering and Enhancing Images Chang-Su Kim Chapter 5, Computer Vision by Shapiro and Stockman Note: Some figures and contents in the lecture notes of Dr. Stockman are used partly.
More informationMotivation. Intensity Levels
Motivation Image Intensity and Point Operations Dr. Edmund Lam Department of Electrical and Electronic Engineering The University of Hong ong A digital image is a matrix of numbers, each corresponding
More information