Fundamentals of Image Processing & Computer Vision with
MATLAB
By
Ali [email protected]
1
Introduction to MATLABImage ProcessingImage Processing MATLAB
ToolboxImage Processing ExamplesComputer VisionComputer Vision
MATLAB ToolboxComputer Vision ExamplesApplicationsAlternatives to
MATLABOverview2
MATLAB(matrixlaboratory) is amulti-paradigmnumerical computing
environment andfourth-generation programming language developed by
MathWorks.
MATLAB allowsmatrixmanipulations, plotting offunctionsand data,
implementation ofalgorithms, creation ofuser interfaces, and
interfacing with programs written in other languages,
includingC,C++, Java, Fortran andPython.
Introduction to MATLAB
3
MATLAB User Interface
Command WindowCurrent FolderWorkspace
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/variablesIntroduction to MATLAB5
>> verDisplays 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 MATLAB6
Basic Arithmatics ( + , - , * , / )
Variables
every variable is an array or matrix
>> x=10Creates 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 MATLAB7
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.
>> 212/23ans = 9.2174
You can use this variable ans
>> ans+5ans = 14.2174Introduction to MATLAB8
who command displays all the variable names you have
used>> whoYour 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
doubleIntroduction to MATLAB9
Constants
>> pians = 3.1416
>> epsans = 2.2204e-16
Introduction to MATLABSymbolic Math Toolbox
>> x=1/3x = 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>> 4^3ans = 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 = 3Vectors13
>> a.*bans = 3 16 35 60 91
>> p=a.^2p = 1 16 49 100 169
>> sqrt(p)ans = 1 4 7 10 13Introduction to MATLAB
Vectors>> a=1:3:15a = 1 4 7 10 13
>> a(2:5)ans = 4 7 10 13
>> b=(3:7)b = 3 4 5 6 714
>> n(2,3)ans = 4
>> n(3,:)ans = 7 8 6
>> max(n)ans = 7 9 6Introduction to MATLAB
>> min(n)ans = 2 5 1
>> sum(n)ans = 12 22 11
>> sum(sum(n))ans = 45Matrices>> 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 6Matrices>>
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))+20ans = 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 =
676.5667Introduction 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
= 42.500018
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 MATLABfunction max = mymax(n1, n2, n3)max =
n1;if(n2 > max) max = n2;endif(n3 > max) max =
n3;enddisp(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)- ------------------------------ +
---------------------------- 2 221
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 =1/223
Plotting
>> x=[4 6 8 11 12];>> y=[1 3 3 5 7];>>
plot(x,y)>> bar(y)>> pie(x)Introduction to MATLAB24
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: Aliname =Ali
>> whos Name Size Bytes Class name 1x19 38 char25
Decisions
>> a = 100; if a < 20 disp('less than 20'); else
disp('not less than 20'); end disp(a);
not less than 20 100Introduction to MATLAB>> grade = 'B';
switch(grade) case 'A' disp('A'); case 'B' disp('B'); case 'C'
disp('C'); otherwise disp('Invalid grade'); end26
Loops
>> for a = 1.0: -0.1: 0.0 disp(a)end
1 0.9000 0.8000 . 0.1000 0Introduction to MATLAB>> a =
10;while( a < 20 ) disp(a) a = a + 2;end
10 12 14 16 1827
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 consideredhigh-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
FeaturesImage Processing Toolbox supports a diverse set of image
typesimage analysisimage segmentationimage enhancement noise
reductiongeometric 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, wherethe first plane in the third
dimensionrepresents the red pixel intensities,the second plane
represents the greenpixel intensities, and the third
planerepresents the blue pixel intensities.
Images in MATLAB31
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
graphicalrepresentation of the tonal distribution in a digital
image.The horizontal axis of the graph represents the tonal
variations, while thevertical axis represents the number of pixels
in that particular tone.
Histogram of Image
34
Clc;clear;I = imread('pout.tif'); %reading an
imagefigure;subplot(2,2,1);imshow(I); %shows the
imagesubplot(2,2,2);imhist(I); %Views the distribution of image
pixel intensitiesI2 = 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
imagefigure;subplot(2,2,1)imshow(I) %shows the
imagesubplot(2,2,2)imhist(I) %Views the distribution of image pixel
intensities I3=imadjust(I); %Stretches the
histogramsubplot(2,2,3)imshow(I3)subplot(2,2,4)imhist(I3)Enhancement
(2)37
Enhancement (2)38
histeqBy 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)
imadjustimadjust 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. imadjust39
clc;
clear;I=imread('cameraman.tif');subplot(1,3,1)imshow(I)title('original')
V= I(end:-1:1,:); % Vertical
Flipsubplot(1,3,2)imshow(V)title('vertical')
Flip40
H= I(:,end:-1:1); % Horizontal
Flipsubplot(1,3,3)imshow(H)title('Horizontal')
Flip
41
scale = 0.5;R = imresize(I,scale); % Resizingfigure;
imshow(R)title('Resized') theta = 35;Rotation = imrotate(I,theta);
% Rotatingfigure;
imshow(Rotation)title('Rotated')Imwrite(Rotation,'r.png','png') %
writes image data to the file % specified by filenameRotate,
Resize42
Rotate, Resize
43
Thresholding44
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 channelgreen=I(:,:,2); %extracts the green
channelblue=I(:,:,3); %extracts the blue
channelfigure;subplot(1,3,1); imshow(red);
title('red');subplot(1,3,2); imshow(green);
title('green');subplot(1,3,3); imshow(blue); title('blue');
Thresholding46
RGB Channels
Thresholding
47
figure;pix=impixel(I); %the tool for getting pixel
information
Thresholding
48
maxPixels=max(pix); %finds the maximum of
columnsminPixels=min(pix); %finds the minimum of columns out =
red=minPixels(1) &... green=minPixels(2) &... blue=
minPixels(3); %trying to thershold the selected segment
figure;imshow(out);
Thresholding49
Thresholding50
out2 = imfill(out,'holes'); % filling the
holesfigure;imshow(out2);title('without holes'); stats =
regionprops(out2); %information about the segment
Thresholding51
Thresholding52
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 filteringThe 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');
Sharpening55
Sharpening
56
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
linesc1=imfilter(b,h);figure; imshow(c1);h=[-1 2 -1;-1 2 -1; -1 2
-1]; %filter for vertical linesc2=imfilter(b,h);figure;
imshow(c2);
Line Detection60
Line Detection61
d=imadd(c1,c2); %adding two imagesfigure; imshow(d);
Line Detection
62
clc; clear;a=imread('house.jpg');b=rgb2gray(a); %converts the
RGB image to grayscalefigure; 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 Vision67
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 Toolbox68
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 Circles69
Find Bright & Dark Circles
70
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 imagerecognizedText = ocrResults.Text; %the Text
fieldfigure;imshow(I);disp(recognizedText) %displays the whole text
in Command Window
OCR72
OCR73
boxed =
insertObjectAnnotation(I,'rectangle',ocrResults.WordBoundingBoxes,
ocrResults.WordConfidences); %showing the boxes around the words
with the %relevant word confidencefigure;imshow(boxed)
OCR74
OCRRan ZhangXuemin (Sherman) Shen
Mobile ElectricVehicles
Online Charging and Discharging75
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 Detection76
clc; clear; for c=1:7I=imread(num2str(c),'jpg');
detector=vision.CascadeObjectDetector; box = step(detector, I);out
= insertObjectAnnotation(I,'rectangle',box,'face');figure;
imshow(out);end
Face Detection77
Face Detection78
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.SupportedFormatsvid
=
videoinput('winvideo',1,'YUY2_640x480');preview(vid)Stop(vid)Video
Acquisition85
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 stream86
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 stream87
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 stream88
Pattern Recognition is a branch of machine learning that focuses
on the recognition of patterns andregularities in data, although it
is in some casesconsidered to be nearly synonymouswith machine
learning.
Pattern Recognition algorithms generally aimto provide a
reasonable answer for all possibleinputs and to perform "most
likely" matching ofthe 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 ControlApplications
94
Robotics
Applications
95
Computer Aided Surgery
Applications
96
Geospatial ComputingApplications
97
Military
Applications
98
MathematicaGNU OctaveR (programming language)ScilabSciPy &
NumPy
Alternatives to MATLAB99
Thanks!Any Question?100
