introductory digital image processing using matlab, iit roorkee

Welcome, students!

Let’s learn something cool!

Is this cool?

Meet Pranav MistryFather of Sixth Sense Technology

PhD, MIT Media LabsM.Des, IIT BombayBE, Computer Science, Nirma Institute of Technology

Why Digital Image Processing?

Machine Learning

Gesture Control

Computer VisionFace

Recognition

Introduction To Digital Image Processing 4

So, Let’s start: Lecture Overview

Lecture 1: Monday, 28 Oct, 2013Duration : 90 Mins

Topics:

1. Basic Introduction and Matlab

2. Image Handling

3. Operations on Images

4. Sample Exercises

Lecture 2: Tuesday, 29 Oct, 2013Duration : 90 Mins

Topics:

5. Noise Filtering and Segmentation

6. Colour Image Analysis

7. Gesture Recognition- Case Study

8. Textbook and beyond

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1.Basic Introduction and Matlab

Digital Image: The digital image is essentially a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding quantized values that represent the brightness of a given color at any specific point.

-Monochrome/Grayscale Image has 1 value per pixel, while Colour Image has 3 values per pixel. (R, G, B)

monochrome image= f( x, y)-where x and y are spatial coordinates-f gives amplitude of intensity

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-Colour image= 3 R,G,B monochrome images

where, Pixel Location: p = (r , c)Pixel Intensity Value: I(p) = I(r , c)

Notation to represent a pixel : [ p, I(p)]

Note: Origin in case of Matlab Image Processing Toolbox is not p=(0,0) but p=(1,1)

Introduction To Digital Image Processing 710/25/2013

The MATrix LABoratory

MATLAB is a dynamically typed language-Means that you do not have to declare any variables-All you need to do is initialize them and they are created

MATLAB treats all variables as matrices-Scalar – 1 x 1 matrix. Vector – 1 x N or N x 1 matrix-Why? Makes calculations a lot faster

These MATRICES can be manipulated in two ways:-Matrix Manipulation (usual matrix operations)-Array Manipulation (using dot (.) operator as prefix)-Vector Manipulation

Cleve Moler Stanford University1970


Basics of MATrix LABoratory

What is the best way to learn Matlab?- Using the ‘Help’ file (sufficient for 90%

operations)- Practicing along with it.

Common Errors:1. Select ‘Image Processing Toolbox’ before starting to use various image functions.(only available with 2008b or newer) 2. Always make sure whether current folder is same as desired.3. Forgetting to use help command


2. Image Handling

Matlab Functions function[output]=name(inputs)

- Create and save new ‘.m’ file in the current directorySome inbuilt functions (in Image Pro. Toolbox) to remember:

- help, clc, type- Imread(‘filename.ext’)- imwrite(g,‘filename.ext’,’compression’,’parameter’,’resolution’,

[colores,rowres],‘quality’)- mat2gray(f,[fmin,fmax])- imshow(f(:,:,x))- figure -for holding on to an image and displaying other (used

as prefix with,)- whos -for displaying size of all images currently being used

in workspace

If a statement doesn’t fit a line, we use ‘…’ , to indicate it continues in next line

Question: How to find intensity of a given pixel?


Image Handling (Continued)

-Accessing subset of an image: For monochromatic images: im2 = im(row1:row2,col1:col2);For colour images: im2 = im(row1:row2,col1:col2,:);

-Resizing an image: out = imresize(im, scale, ‘method’); or out = imresize(im, [r c], ‘method’);

-Rotating an image: out = imrotate(im, angle_in_deg,

‘method’);

Methods to fill lacking data:1.’nearest’= as neighborhood2.’bilinear’=linear interpolation3.’bicubic’=cubic int. (*best)


3. Operation on Images : Transformations

G( x, y) = T [f ( x, y)]where, G= Processed Image

T= Operatorf=input image

-Brightness/Intensity Transformation: im2=c*im; im2=im+c;

If c > 1, c>0 increasing brightnessIf c < 1, c<0 decreasing brightness

-Contrast Transformation: out = imadjust(im, [], [], gamma);

Contrast represents how the intensity changes from min to max value.


Operations on Images: Spatial Filtering

1. First we need to create an N x N matrix called a mask, kernel, filter (or neighborhood).2. The numbers inside the mask will help us control the kind of operation we’re doing.3. Different numbers allow us to blur, sharpen, find edges, etc.4. We need to master convolution first, and the rest is easy!

a.k.a. neighborhood processing

Maska b cd e fg h i G=[

]H=[ ]

z y xw v ut s r out = a*z + b*y + c*x + d*w + e*v + f*u + g*t + h*s + i*r,


Application:

Before

After

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What do we observe?


Application: Blurring

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Blurring:- Reduces noise (high frequency)- Reduces edges (high

frequency)- Is essentially a low pass filter

(eliminates high f)

- Can be done through averaging filter

- For colour images, we can blur each layer independentlymask = fspecial(‘average’, N);

out = imfilter(im, mask,’conv’);

More theMask size, More blur inResult


Application: Edge Detection

What is an edge? – ‘A place of change’.

f’( x, y)= f( x-1, y) – f( x+1,y)This is a Horizontal filter. (puts more weight on central

pixel)

How do we do this in MATLAB?1) Create our Prewitt or Sobel Horizontal Masks: mask = fspecial(‘prewitt’); or mask = fspecial(‘sobel’);2) Convolve with each mask separately dX = imfilter(im, mask); dY = imfilter(im, mask2);3)Find the overall magnitude mag = sqrt(double(dX).^(2) + double(dY).^(2));

EXERCISE: Use following masks in fspecial function and find out what they do- Gaussian, Laplacian, Laplacian of Gaussian (LoG)

Alternate way: Canny Edge Detector

(most powerful edge detector)

[ g, t]= edge (f, ‘canny’, T , sigma)


4. Noise Filtering

▪ In reality, pixel corruption takes place during process of acquisition or transmission. There is a need to remove(okay, ‘try to’) this noise.

▪ As an exercise, let’s add up artificial noise in an image using function:

n = imnoise ( im, ‘salt & pepper’, density);

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Gaussian

PoissonSalt & Pepper

Use blurring filter against ‘Gaussian’ noise. Use median filter against ‘salt & pepper noise’.

out = medfilt2( n , [M N]);


Segmentation

▪ This division is done mainly on the basis of :

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division of an image into segments or parts (region of interests)

(a) grey level (b) texture (c) motion

(d) depth

(e) colour

Can you think of ways in which this will prove useful?


Segmentation Techniques

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One way is already covered. Can you name that? Thresholding : Simplest Segmentation Technique Pixels are grouped into “Object” and “Background”– Input: Gray scale image– Output: Binary image

Implementing in Matlab: output = im2bw(Image, k)

where, K=T/largest pixel size

Other Methods:1. Region Growing: A method that clubs similar property pixels. (Satellites)2. Watershed Transform: grayscale intensity is interpreted as distance. (topographical use)


Colour Image Analysis

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Trichromacy theory :All colors found in nature can naturally be decomposed into Red, Green and Blue.

Other models: CMY, NTSC, YCbCr, HSI, CMYK, HSV

RGB Cube


Colour Image Analysis

▪ Basic Conversions:

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What is an indexed image?An image having two components:1. Data Matrix2. Colour Map (a.k.a. ‘palette’)What is its use?1.To make display process fast2.To reduce size of image

Loss of information due to size of palette


Gesture Recognition- Case Study


Low Level• Noise Reduction• Contrast

EnhancementMid Level• Segmentation• Recognition

High Level• Making Sense

Textbooks and beyond:

DIP using Matlab

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