Fundamentals of Image Processing & Computer Vision with MATLAB

By Ali Ghanbarzadeh ghanbarzade.ali@gmail.com 1

 Introduction to MATLAB  Image Processing  Image Processing MATLAB Toolbox  Image Processing Examples  Computer Vision  Computer Vision MATLAB Toolbox  Computer Vision Examples  Applications  Alternatives to MATLAB Overview 2

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language developed by MathWorks. MATLAB allows matrixmanipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Introduction to MATLAB 3

MATLAB User Interface • Command Window • Current Folder • Workspace Introduction to MATLAB 4

>> clc Clears command window >> help Displays help text in Command Window >> clear Clears variables and functions from memory. removes all variables from the workspace. >> clear ‘a variable/variables’ Introduction to MATLAB 5

>> ver Displays MathWorks product family header information, followed by the current MATLAB, Simulink and toolbox version information to write comments use % >> %This is a comment Introduction to MATLAB 6

Basic Arithmatics ( + , - , * , / ) Variables every variable is an array or matrix >> x=10 Creates a 1-by-1 matrix named x and stores the value 10 in its element. Once a variable is entered into the system, you can refer to it later. Variables must have values before they are used. Introduction to MATLAB 7

When an expression returns a result that is not assigned to any variable, the system assigns it to a variable named ans, which can be used later. >> 212/23 ans = 9.2174 You can use this variable ans >> ans+5 ans = 14.2174 Introduction to MATLAB 8

 who command displays all the variable names you have used >> who Your variables are: a ans b x whos command gives more information >> whos Name Size Bytes Class a 1x1 8 double ans 1x1 8 double b 1x1 8 double x 1x1 8 double Introduction to MATLAB 9

Constants >> pi ans = 3.1416 >> eps ans = 2.2204e-16 Introduction to MATLAB  Symbolic Math Toolbox >> x=1/3 x = 0.3333 >> x=sym(1/3) x = 1/3 >> double(x) ans = 0.3333 10

Trigonometric Functions >> sin(pi/6) ans = 0.5000 >> cos(0) + tan(pi/4) ans = 2 Introduction to MATLAB 11

>> exp(1) ans = 2.7183 >> log(exp(1)) ans = 1 >> log10(1000) ans = 3 Introduction to MATLAB >> 4^3 ans = 64 >> sqrt(81) ans = 9 >> nthroot(625,4) ans = 5 >> log2(1024) ans = 10 >> factorial (5) ans = 120 12

>> m(3) ans = 3 >> length(m) ans = 5 >> sum(m) ans = 15 Introduction to MATLAB >> m=[1 2 3 4 5] m = 1 2 3 4 5 >> m' ans = 1 2 3 4 5 >> min(m) ans = 1 >> max(m) ans = 5 >> mean(m) ans = 3  Vectors 13

>> a.*b ans = 3 16 35 60 91 >> p=a.^2 p = 1 16 49 100 169 >> sqrt(p) ans = 1 4 7 10 13 Introduction to MATLAB  Vectors >> a=1:3:15 a = 1 4 7 10 13 >> a(2:5) ans = 4 7 10 13 >> b=(3:7) b = 3 4 5 6 7 14

>> n(2,3) ans = 4 >> n(3,:) ans = 7 8 6 >> max(n) ans = 7 9 6 Introduction to MATLAB >> min(n) ans = 2 5 1 >> sum(n) ans = 12 22 11 >> sum(sum(n)) ans = 45  Matrices >> n=[3 5 1;2 9 4;7 8 6] n = 3 5 1 2 9 4 7 8 6 >> n(1:3,2:3) ans = 5 1 9 4 8 6 15

>> diag(n) ans = 3 9 6 >> det(n) ans = 99.0000 Introduction to MATLAB >> triu(n) ans = 3 5 1 0 9 4 0 0 6 >> tril(n) ans = 3 0 0 2 9 0 7 8 6  Matrices >> numel(n) ans = 9 >> size(n) ans = 3 3 >> n' ans = 3 2 7 5 9 8 1 4 6 16

>> (30-20)*(rand(5,1))+20 ans = 21.5761 29.7059 29.5717 24.8538 28.0028 >> ones(2) ans = 1 1 1 1 Introduction to MATLAB >> zeros(3) ans = 0 0 0 0 0 0 0 0 0 >> eye(3) ans = 1 0 0 0 1 0 0 0 1  Matrices >> rand(2) ans = 0.8147 0.1270 0.9058 0.9134 >> rand(3,2) ans = 0.6324 0.5469 0.0975 0.9575 0.2785 0.9649 17

>> std(data) ans = 26.0109 >> var(data) ans = 676.5667 Introduction to MATLAB  Statistics >> data=[12,54,23,69,31,76] data = 12 54 23 69 31 76 >> sort(data) ans = 12 23 31 54 69 76 >> median(ans) ans = 42.5000 18

Functions >> area_rectangle=inline('2*(a+b)','a','b') area_rectangle = Inline function: area_rectangle(a,b) = 2*(a+b) >> area_rectangle(5,7) ans = 24 Introduction to MATLAB function max = mymax(n1, n2, n3) max = n1; if(n2 > max) max = n2; end if(n3 > max) max = n3; end disp(max); 19

Calculus – differentiation >> syms x >> f=inline('sin(x)+2*cos(x)','x') f = Inline function: f(x) = sin(x)+2*cos(x) >> diff(f(x),x) ans = cos(x) – 2*sin(x) Introduction to MATLAB >> syms x y >> f=inline('sin(x)+cos(y)','x','y') f = Inline function: f(x,y) = sin(x)+cos(y) >> diff(f(x,y),x) ans = cos(x) 20

Calculus - integral >> syms x >> f=inline('x/sin(x^2)','x') f = Inline function: f(x) = x/sin(x^2) >> integral=int(f(x),x) integral = - log(- x*2*i - x*exp(x^2*i)*2*i)/2 + log(x*2*i - x*exp(x^2*i)*2*i)/2 Introduction to MATLAB >> pretty(integral) 2 2 log(- x 2 i - x exp(x i) 2 i) log(x 2 i - x exp(x i) 2 i) - ------------------------------ + ---------------------------- 2 2 21

Calculus – integral >> syms x >> f=inline('(x^3)+2','x') f = Inline function: f(x) = (x^3)+2 >> integral=int(f(x),x,2,4) integral = 64 Introduction to MATLAB 22

Calculus – limit >> syms x >> f=inline('sin(x)/x','x') f = Inline function: f(x) = sin(x)/x >> limit(f(x),x,0) ans = 1 Introduction to MATLAB >> syms x >> g=inline('(x+x^2)/((2*x^2)+3*x) ','x') g = Inline function: g(x) = (x+x^2)/((2*x^2)+3*x) >> limit(g(x),x,Inf) ans = 1/2 23

Plotting >> x=[4 6 8 11 12]; >> y=[1 3 3 5 7]; >> plot(x,y) >> bar(y) >> pie(x) Introduction to MATLAB 24

input >> a=input('enter value for array: ') enter value for array: [3 4 9 0 2] a = 3 4 9 0 2 Introduction to MATLAB >> name=input('enter your name: ','s') enter your name: Ali name = Ali >> whos Name Size Bytes Class name 1x19 38 char 25

Decisions >> a = 100; if a < 20 disp('less than 20'); else disp('not less than 20'); end disp(a); not less than 20 100 Introduction to MATLAB >> grade = 'B'; switch(grade) case 'A' disp('A'); case 'B' disp('B'); case 'C' disp('C'); otherwise disp('Invalid grade'); end 26

Loops >> for a = 1.0: -0.1: 0.0 disp(a) end 1 0.9000 0.8000 …. 0.1000 0 Introduction to MATLAB >> a = 10; while( a < 20 ) disp(a) a = a + 2; end 10 12 14 16 18 27

Image Processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, and the output of image processing may be either an image or a set of characteristics or parameters related to the image. Image processing usually refers to digital image processing, but optical and analog image processing also are possible. Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects instead of being acquired via imaging devices. Computer vision is considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images. Image Processing 28

Features Image Processing Toolbox supports a diverse set of image types image analysis image segmentation image enhancement  noise reduction geometric transformations Visualization functions and apps let you explore images and videos, examine a region of pixels, adjust color and contrast, create contours or histograms Image Processing MATLAB Toolbox 29

The basic data structure in MATLAB is the array. MATLAB stores most images as two-dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a single pixel in the displayed image. Images in MATLAB 30

Some images, such as truecolor images, require a three-dimensional array, where the first plane in the third dimension represents the red pixel intensities, the second plane represents the green pixel intensities, and the third plane represents the blue pixel intensities. Images in MATLAB 31

I=imread('balls.jpg'); imtool(I); Exploring Images (Measurement) 32

Exploring Images (Inspect Pixels Value) 33

An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. The horizontal axis of the graph represents the tonal variations, while the vertical axis represents the number of pixels in that particular tone. Histogram of Image 34

Clc;clear; I = imread('pout.tif'); %reading an image figure; subplot(2,2,1); imshow(I); %shows the image subplot(2,2,2); imhist(I); %Views the distribution of image pixel intensities I2 = histeq(I); %improves the contrast in an image,using the histeq %function. subplot(2,2,3); imshow(I2); subplot(2,2,4); imhist(I2); Enhancement (1) 35

imwrite (I2,'pout2.png','png'); %Writes the newly adjusted image to a %file. imfinfo('pout2.png'); %Gives the information about the image Enhancement (1) 36

clc; clear; I = imread('pout.tif'); %reading an image figure; subplot(2,2,1) imshow(I) %shows the image subplot(2,2,2) imhist(I) %Views the distribution of image pixel intensities I3=imadjust(I); %Stretches the histogram subplot(2,2,3) imshow(I3) subplot(2,2,4) imhist(I3) Enhancement (2) 37

histeq By default, MATLAB transforms the probability distribution to a uniform distribution, which means all the intensity ranges have equivalent probabilities. The histogram is modified to have the counts of all pixels close to each other (uniform distribution) imadjust imadjust just "Stretches" the histogram of an image in order to have the intensity range of the image fill the entire available range (normally 0-255). histeq vs. imadjust 39

clc; clear; I=imread('cameraman.tif'); subplot(1,3,1) imshow(I) title('original') V= I(end:-1:1,:); % Vertical Flip subplot(1,3,2) imshow(V) title('vertical') Flip 40

H= I(:,end:-1:1); % Horizontal Flip subplot(1,3,3) imshow(H) title('Horizontal') Flip 41

scale = 0.5; R = imresize(I,scale); % Resizing figure; imshow(R) title('Resized') theta = 35; Rotation = imrotate(I,theta); % Rotating figure; imshow(Rotation) title('Rotated') Imwrite(Rotation,'r.png','png') % writes image data to the file % specified by filename Rotate, Resize 42

Thresholding is the simplest method of image segmentation. From a grayscale image, thresholding can be used to create binary images. The simplest thresholding methods replace each pixel in an image with a black pixel if the image intensity 𝐼𝑖,𝑗 is less than some fixed constant T(that is 𝐼𝑖,𝑗 < T), or a white pixel if the image intensity is greater than that constant. Thresholding 44

The approach for thresholding color images is to designate a separate threshold for each of the RGB components of the image and then combine them with an AND operation. Thresholding 45

clc; clear; I=imread('balls.jpg'); red=I(:,:,1); %extracts the red channel green=I(:,:,2); %extracts the green channel blue=I(:,:,3); %extracts the blue channel figure; subplot(1,3,1); imshow(red); title('red'); subplot(1,3,2); imshow(green); title('green'); subplot(1,3,3); imshow(blue); title('blue'); Thresholding 46

figure; pix=impixel(I); %the tool for getting pixel information Thresholding 48

maxPixels=max(pix); %finds the maximum of columns minPixels=min(pix); %finds the minimum of columns out = red<=maxPixels(1) & red>=minPixels(1) &... green<=maxPixels(2) & green>=minPixels(2) &... blue<=maxPixels(3) & blue>= minPixels(3); %trying to thershold the selected segment figure; imshow(out); Thresholding 49

out2 = imfill(out,'holes'); % filling the holes figure; imshow(out2); title('without holes'); stats = regionprops(out2); %information about the segment Thresholding 51

In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature. fspecial Creates predefined 2-D filter Filters 53

medfilt2 2-D median filtering The median filter is a nonlinear digital filtering technique, often used to remove noise. Filters 54

clc; clear; I=imread('cat.png'); figure; imshow(I);title('Original Image'); motion_f=fspecial('motion',10); I2=imfilter(I,motion_f); figure; imshow(I2);title('Blurred Image'); I3=imsharpen(I2,'amount',5); figure;imshow(I3);title('Sharpened Image'); Sharpening 55

clc; clear; I=imread('mo.jpg'); I=rgb2gray(I); figure;imshow(I); Filter Noise 57

N=imnoise(I,'salt & pepper',0.25); figure,imshow(N); average_f=fspecial('average'); Filtered=imfilter(N,average_f); figure,imshow(Filtered); Filter Noise 58

Filtered=medfilt2(N); figure,imshow(Filtered); Filter Noise 59

clc; clear; a=imread('shelf.png'); b=rgb2gray(a); figure; imshow(a); h=[-1 -1 -1;2 2 2; -1 -1 -1]; %filter for horizontal lines c1=imfilter(b,h); figure; imshow(c1); h=[-1 2 -1;-1 2 -1; -1 2 -1]; %filter for vertical lines c2=imfilter(b,h); figure; imshow(c2); Line Detection 60

d=imadd(c1,c2); %adding two images figure; imshow(d); Line Detection 62

clc; clear; a=imread('house.jpg'); b=rgb2gray(a); %converts the RGB image to grayscale figure; imshow(b); Edge Detection 63

e=edge(b,'sobel'); %edge detection with 'sobel' figure; imshow(e); Edge Detection 64

e=edge(b,'canny'); %edge detection with 'canny' figure; imshow(e); Edge Detection 65

h=fspecial('laplacian'); e=imfilter(b,h); figure; imshow(e); Edge Detection 66

Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner. Computer Vision 67

Computer Vision System Toolbox provides algorithms, functions, and apps for the design and simulation of computer vision and video processing systems. You can perform object detection and tracking, feature detection and extraction, feature matching, camera calibration, and motion detection tasks. The system toolbox also provides tools for video processing, including video file I/O, video display, object annotation, drawing graphics. Computer Vision MATLAB Toolbox 68

clc; clear; A = imread('circlesBrightDark.png'); figure;imshow(A); Rmin = 30; Rmax = 65; [centersBright, radiiBright] = imfindcircles(A,[Rmin … Rmax],'ObjectPolarity','bright'); [centersDark, radiiDark] = imfindcircles(A,[Rmin … Rmax],'ObjectPolarity','dark'); figure;imshow(A); viscircles(centersBright, radiiBright,'EdgeColor','b'); viscircles(centersDark, radiiDark,'LineStyle','--'); Find Bright & Dark Circles 69

Optical character recognition (optical character reader) (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text. data entry from printed paper data records, whether passport documents, invoices, bank statements, computerised receipts, business cards, mail, printouts of static-data, or any suitable documentation. used in machine processes such as machine translation, text-to-speech and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision. OCR (Optical Character Recognition) 71

clc; clear; I = imread('ocr_test.png'); ocrResults = ocr(I); %OCR for getting the text out of an image recognizedText = ocrResults.Text; %the Text field figure; imshow(I); disp(recognizedText) %displays the whole text in Command Window OCR 72

boxed = insertObjectAnnotation(I,'rectangle',… ocrResults.WordBoundingBoxes,… ocrResults.WordConfidences); %showing the boxes around the words with the %relevant word confidence figure; imshow(boxed) OCR 74

OCR Ran Zhang Xuemin (Sherman) Shen Mobile Electric Vehicles Online Charging and Discharging 75

The cascade object detector uses the Viola-Jones algorithm to detect people's faces, noses, eyes, mouth, or upper body. detector = vision.CascadeObjectDetector creates a System object, detector, that detects objects using the Viola-Jones algorithm. The ClassificationModel property controls the type of object to detect. By default, the detector is configured to detect faces. BBOX = step(detector,I) returns BBOX, an M-by-4 matrix defining M bounding boxes containing the detected objects. This method performs multiscale object detection on the input image, I. Each row of the output matrix, BBOX, contains a four-element vector, [x y width height], that specifies in pixels, the upper-left corner and size of a bounding box. The input image I, must be a grayscale or truecolor (RGB) image. Cascade Object Detection 76

clc; clear; for c=1:7 I=imread(num2str(c),'jpg'); detector=vision.CascadeObjectDetector; box = step(detector, I); out = insertObjectAnnotation(I,'rectangle',box,'face'); figure; imshow(out); end Face Detection 77

clc; clear; I1=imread('b2.png'); I2=imread('sm.jpg'); detector=vision.CascadeObjectDetector('EyePairBig'); detector.MergeThreshold=10; box1 = step(detector, I1); box2 = step(detector, I2); out1 = insertObjectAnnotation(I1,'rectangle',box1,'eyes'); figure; imshow(out1); out2 = insertObjectAnnotation(I2,'rectangle',box2,'eyes'); figure; imshow(out2); Face Feature Detection(Eyes) 79

Face Feature Detection(Eyes) 80

clc; clear; I1=imread('b2.png'); I2=imread('sm.jpg'); detector=vision.CascadeObjectDetector('Mouth'); detector.MergeThreshold=200; box1 = step(detector, I1); box2 = step(detector, I2); out1 = insertObjectAnnotation(I1,'rectangle',box1,'mouth'); figure; imshow(out1); out2 = insertObjectAnnotation(I2,'rectangle',box2,'mouth'); figure; imshow(out2); Face Feature Detection(Mouth) 81

Face Feature Detection(Mouth) 82

clc; clear; I1=imread('b2.png'); I2=imread('sm.jpg'); detector=vision.CascadeObjectDetector('Nose'); detector.MergeThreshold=50; box1 = step(detector, I1); box2 = step(detector, I2); out1 = insertObjectAnnotation(I1,'rectangle',box1,'nose'); figure; imshow(out1); out2 = insertObjectAnnotation(I2,'rectangle',box2,'nose'); figure; imshow(out2); Face Feature Detection(Nose) 83

Face Feature Detection(Nose) 84

clc; clear; info = imaqhwinfo('winvideo'); info.DeviceInfo(1) info.DeviceInfo.SupportedFormats vid = videoinput('winvideo',1,'YUY2_640x480'); preview(vid) Stop(vid) Video Acquisition 85

clc; clear; faceDetector = vision.CascadeObjectDetector(); obj =imaq.VideoDevice('winvideo', 1, 'YUY2_640x480','ROI', … [1 1 640 480]); set(obj,'ReturnedColorSpace', 'rgb'); figure('menubar','none','tag','webcam'); Face Detection from live video stream 86

while (true) frame=step(obj); bbox=step(faceDetector,frame); boxInserter = vision.ShapeInserter('BorderColor','Custom',... 'CustomBorderColor',[255 255 0]); videoOut = step(boxInserter, frame,bbox); imshow(videoOut,'border','tight'); f=findobj('tag','webcam'); Face Detection from live video stream 87

if (isempty(f)); [hueChannel,~,~] = rgb2hsv(frame); hold off noseDetector = vision.CascadeObjectDetector('Nose'); faceImage = imcrop(frame,bbox); noseBBox = step(noseDetector,faceImage); videoInfo = info(obj); ROI=get(obj,'ROI'); VideoSize = [ROI(3) ROI(4)]; tracker = vision.HistogramBasedTracker; initializeObject(tracker, hueChannel, bbox); release(obj); close(gcf) break end % End of if pause(0.05) end % End of While Face Detection from live video stream 88

Pattern Recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern Recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. Pattern Recognition 89

Biometric Image Processing and Recognition Applications 90

Biometric Image Processing and Recognition Applications 91

Medical Imaging Applications 92

Surveillance Applications 93

Industrial Control Applications 94

Computer Aided Surgery Applications 96

Geospatial Computing Applications 97

Mathematica GNU Octave R (programming language) Scilab SciPy & NumPy Alternatives to MATLAB 99

Fundamentals of Image Processing & Computer Vision with MATLAB

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Fundamentals of Image Processing & Computer Vision with MATLAB