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Intensity Images An intensity image is a data matrix whose values have been scaled to represent intensities. When the elements of an intensity image are of class uint8, or class uint16, they have integer values in the range [0,255] and [0,65535], respectively. If the image is of class double, the values are floating-point numbers. Values of scaled, class double intensity images are in the range [0,1] by convention. <strong>MATLAB</strong> – p. 24/333
Binary Images A binary image is a logical array of 0s and 1s. A numaric array is converted to binary using function logical. B=logical(A) To test if an array is logical we use the islogical function: islogical(C) If C is a logical array, this function returns a 1. Otherwise it returns a 0. <strong>MATLAB</strong> – p. 25/333
Page 1 and 2: MATLAB practice IPCV 2006, Budapest
Page 3 and 4: Chapter 1 Fundamentals MATLAB - p.
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: Image Types • Intensity images
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
Page 53 and 54: M-Files M-Files in MATLAB can be sc
Page 55 and 56: M-Files function [G,x] = planerot(x
Page 57 and 58: Arithmetic Operators Operator Name
Page 59 and 60: Arithmetic Operators Operator Name
Page 61 and 62: Image Arithmetic Functions Function
Page 63 and 64: An Example fd=double(f); gd=double(
Page 65 and 66: An Example >> help improd IMPROD Co
Page 67 and 68: Relational Operations Operator Name
Page 69 and 70: Logical Operators Operator Name & A
Page 71 and 72: Logical Functions Function xor all
Page 73 and 74: 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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