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Contents 1. Fundamentals 2. MATLAB Graphics 3. Intensity Transformations and Spatial Filtering 4. Frequency Domain Processing 5. Edge Detection 6. Morphological Image Processing 7. Color Image Processing MATLAB – p. 2/333
Chapter 1 Fundamentals MATLAB – p. 3/333
Page 1: MATLAB practice IPCV 2006, Budapest
Page 5 and 6: Digital Image Representation An ima
Page 7 and 8: Images as Matrices A digital image
Page 9 and 10: Supported Image Formats Format Desc
Page 11 and 12: Displaying Images imshow(f,G) • i
Page 13 and 14: Displaying Images >> f=imread(’ro
Page 15 and 16: Writing Images imwrite(f, ’filena
Page 17 and 18: Writing Images imfinfo filename >>
Page 19 and 20: Writing Images imwrite(g, ’filena
Page 21 and 22: Writing Images print -fno -dfilefor
Page 23 and 24: Image Types • Intensity images
Page 25 and 26: Binary Images A binary image is a l
Page 27 and 28: Converting between Image Classes an
Page 33 and 34: Array Indexing • Vector Indexing
Page 35 and 36: Vector Indexing >> v(1:3) ans = 1 3
Page 37 and 38: Vector Indexing linspace(a, b, n) >
Page 39 and 40: Matrix Indexing >> C3=A(:,3) C3 = 3
Page 41 and 42: Matrix Indexing >> A(end,end) ans =
Page 43 and 44: Matrix Indexing >> v=T2(:) v = 1 4
Page 45 and 46: Matrix Indexing >> f=imread(’rose
Page 47 and 48: Matrix Indexing >> fs=f(1:8:end,1:8
Page 49 and 50: Selecting Array Dimensions operatio
Page 51 and 52: Some Important Standard Arrays >> A
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M-Files M-Files in MATLAB can be sc
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M-Files function [G,x] = planerot(x
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Arithmetic Operators Operator Name
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Arithmetic Operators Operator Name
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Image Arithmetic Functions Function
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An Example fd=double(f); gd=double(
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An Example >> help improd IMPROD Co
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Relational Operations Operator Name
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Logical Operators Operator Name & A
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Logical Functions Function xor all
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Logical Functions Function Descript
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Some Important Variables and Consta
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if, else, and elseif if expression
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for for index=start:increment:end s
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for for q=0:5:100 filename=sprintf(
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while while expression statements e
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eak fid = fopen(’fft.m’,’r’
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switch switch switch_expression cas
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eturn function d = det(A) %DET det(
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Code Optimization • Vectorizing L
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A Simple Example Suppose that we wa
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Comparison for loops vs. vectorizat
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Comparison for loops vs. vectorizat
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Preallocating Arrays tic for i=1:10
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Interactive I/O t=input(’message
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Interactive I/O [a,b,c,...]=strread
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Load variables from disk load(’fi
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Cell Arrays Cell array is a multidi
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Structures Structures allow groupin
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Plotting Your Data >> x=0:0.2:12; >
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Creating Plots >> t=0:pi/100:2*pi;
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Specifying Line Style >> plot(t,y,
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Adding Plots to an Existing Graph >
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Plotting Markers and Lines >> x=0:p
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Plotting with Two Y-Axes >> t=0:pi/
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Combining Linear and Logarithmic Ax
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Specifying Ticks and Tick Labels 1
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Chapter 3 Intensity Transformations
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Background The spatial domain proce
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Function imadjust g=imadjust(f,[low
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Function imadjust >> g1=imadjust(f,
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Function imadjust >> g3=imadjust(f,
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Generating and Plotting Image Histo
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Some Useful Plotting Function Symbo
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Histogram Equalization g=histeq(f,n
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Histogram Equalization 18000 16000
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Histogram Equalization 1 Output int
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Histogram Matching x 10 4 6 5 4 3 2
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Histogram Matching c1=A1*(1/((2*pi)
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Histogram Matching %Initialize. rep
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Histogram Matching >> f=imread(’m
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Spatial Filtering Neighborhood proc
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Linear Spatial Filtering The proces
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Linear Spatial Filtering The first
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Linear Spatial Filtering The preced
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Linear Spatial Filtering Convolutio
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Linear Spatial Filtering Options Fi
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Linear Spatial Filtering >> gd=imfi
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Linear Spatial Filtering >> gs=imfi
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Linear Spatial Filtering >> f8=im2u
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Nonlinear Spatial Filtering The IPT
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Nonlinear Spatial Filtering g=colfi
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Nonlinear Spatial Filtering Options
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Nonlinear Spatial Filtering functio
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IPT Standard Spatial Filters • Li
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Linear Spatial Filters Type ’aver
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Linear Spatial Filters >> w=fspecia
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Linear Spatial Filters >> g1=imfilt
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Linear Spatial Filters >> g=f2-g2;
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Linear Spatial Filters MATLAB - p.
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Nonlinear Spatial Filters Min filte
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Nonlinear Spatial Filters >> f=imre
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Chapter 4 Frequency Domain Processi
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The 2-D Discrete Fourier Transform
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Computing and Visualizing the 2-D D
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Filtering in the Frequency Domain
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Fundamental Concepts f(x,y) = F −
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Fundamental Concepts The foundation
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Fundamental Concepts Filtering in t
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Fundamental Concepts Based on the c
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Fundamental Concepts Assume that fu
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Fundamental Concepts if nargin==1 P
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Fundamental Concepts The image, f,
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Fundamental Concepts >> PQ=paddedsi
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Basic Steps in DFT Filtering MATLAB
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Basic Steps in DFT Filtering MATLAB
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An M-function for Filtering in the
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Obtaining Frequency Domain Filters
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Edge detection • Edges can be fou
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Prewitt detector Masks: ⎡ ⎢ ⎣
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Roberts detector Masks: [ −1 0 0
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Zero-crossing detector It is very s
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Canny detector 5. Finally, the algo
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Dilation IPT function imdilate perf
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Structuring Element IPT function st
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Structuring Element Syntax Forms st
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Dilation >> originalI=imread(’cam
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Erosion MATLAB - p. 279/333
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Labeling Connected Components >> f=
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Chapter 7 Color Image Processing MA
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Color Image Representation in MATLA
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RGB Images The data class of the co
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RGB Images If all component images
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RGB Images function rgbcube(vx,vy,v
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RGB Images %Set up viewing point. i
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Indexed Images MATLAB - p. 295/333
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Indexed Image Sometimes it is neces
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Indexed Images Name autumn bone col
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Manipulating RGB and Indexed Images
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NTSC Color Space The NTSC Color Sys
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NTSC Color Space Function rgb2ntsc
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The YCbCr Color Space The YCbCr col
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The YCbCr Color Space The conversio
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The HSV Color Space MATLAB - p. 315
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The CMY Color Space The conversion
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The HSI Color Space When humans vie
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Converting Colors from RGB to HSI T
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Converting Colors from RGB to HSI f
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Converting Colors from RGB to HSI n
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Converting Color from HSI to RGB RG
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Converting Color from HSI to RGB BR
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Converting Color from HSI to RGB %
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References • R. C. Gonzalez, R. E
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