Introduction to Image Processing with MATLAB

A QUICK

INTRODUCTION TO IMAGE PROCESSING WITH MATLAB

Presenter,Sriram Emarose

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Vision?Every technology comes from Nature:

Eye - Sensor to acquire photons Brain - Processor to process photoelectric signals from eye

Step 1. Light(white light) falling on objects

Step 2. Eye lens focuses the light on retina

Step 3. Image formation on retina, and

Step 4. Developing electric potential on retina (Photoelectric effect)

Step 5. Optical nerves transmitting developed potentials to brain (Processor)

Natural Sensor (EYE) – Working mechanism

Natural Processor (Brain) – Perception of image

Optic nerves – Transmission medium

Hey, I got potentials of X

values(Temporal lobe)

Yes, I know what does it mean(Frontal lobe)

To frontal lobe,From Temporal

lobe

Neuronal Communication and logical analysing in brain

Different species absorbs different spectral wavelength

Which implies different sensors(eye) have different reception abilities

Light Spectra

Color of the images depends on the type photo receptors

Primary color images – RGB Photoreceptor – Cones

Image colors

Gray scale images (commonly known as black and white ) Photoreceptor - Rods

Guess why it takes time to see objects in dark after being in bright light?

Enough understanding nature?

Camera Man made technology that mimics operation of an eye

Array of photoreceptors and film (to act as retina - cones and rods)

Lens to focus light from surrounding/objects on the photoreceptors (mimics Iris and eye lens)

Camera with CCD sensors

Camera with CMOS sensors

Computer perception of an image

)1,1()1,1()0,1(

)1,1()1,1()0,1(

)1,0()1,0()0,0(

),(

NMfMfMf

Nfff

Nfff

yxf

Gray line – Continuous analog signals from sensors

Dotted lines – Sampling time

Red line – Quantized signal

Digital representation of image obtained from the quantized signal

Got the image matrix, what next?Different types of images often used,

Color – RGB -> remember cones in eyes? R –> 0-255 G –> 0-255 B –> 0-255

Grayscale -> remember rods in eyes? 0 – Pure black/white 1-254 – Shades of black and white(gray) 255 – Pure black/white

Boolean 0- Pure black/white 1- Pure white/black

What do you see?

Single pixel with respective RGB values

RGB Image

Lets zoom out

Combination of RGB values of each pixel contributing to form an image

Grayscale image

Pure black->0Shades of black&white -> 1-254White-> 255

Grayscale image

Binary image

Bored with fundamentals?

LETS DO SOMETHING INTERESTING!

Things to keep in mind,

Image -> 2 dimensional matrix of size(mxn) Image processing -> Manipulating the values of each element of the matrix

)1,1()1,1()0,1(

)1,1()1,1()0,1(

)1,0()1,0()0,0(

),(

NMfMfMf

Nfff

Nfff

yxf

From the above representation, f is an image f(0,0) -> single pixel of an image (similarly for all values of f(x,y)

f(0,0) = 0-255 for grayscale 0/1 for binary 0-255 for each of R,G and B

SIMPLE ISN`T IT!

Extracting objects of specific colors from an imageFrom the image given below, how specific color(say blue) can be extracted?

Algorithm:

Load an RGB image

Get the size(mxn) of the image

Create a new matrix of zeros of size mxn

Read the values of R,G,B in each pixel while traversing through every pixels of the image

Restore pixels with required color to 1 and rest to 0 to the newly created matrix

Display the newly created matrix and the resultant image would be the filtered image of specific color

Solution!Input image:

Output image(Extracted blue objects):

Snippet:

c=imread('F:\matlab sample images\1.png');[m,n,t]=size(c); tmp=zeros(m,n);for i=1:m for j=1:n if(c(i,j,1)==0 && c(i,j,2)==0 && c(i,j,3)==255) tmp(i,j)=1; end endendimshow(tmp);

Count Number of objects in red colorFrom the image, count number of red objects,

Algorithm:

Load the image

Get the size of the image

Find appropriate threshold level for red color

Traverse through every pixel,

Replace pixels with red threshold to 1 and remaining pixels to 0

Find the objects with enclosed boundaries in the new image

Count the boundaries to know number of objects

Solution!Input image:

Output image(Extracted red objects):

Snippet:c=imread('F:\matlab sample images\1.png');[m,n,t]=size(c);tmp=zeros(m,n);for i=1:m for j=1:n if(c(i,j,1)==255 && c(i,j,2)==0 && c(i,j,3)==0) tmp(i,j)=1; end endendimshow(tmp);ss=bwboundaries(tmp);num=length(ss);Output: num = 3

How to count all objects irrespective of color?

Thresholding is used to segment an image by setting all pixels whose intensity values are above a threshold to a foreground value and all the remaining pixels to a background value.

The pixels are partitioned depending on their intensity value

Global Thresholding,g(x,y) = 0, if f(x,y)<=Tg(x,y) = 1, if f(x,y)>T

THRESHOLDING!

g(x,y) = a, if f(x,y)>T2g(x,y) = b, if T1<f(x,y)<=T2 g(x,y) = c, if f(x,y)<=T1

Multiple thresholding,

From the given image, Find the total number of objects present?

Algorithm:

Load the image

Convert the image into grayscale(incase of an RGB image)

Fix a certain threshold level to be applied to the image

Convert the image into binary by applying the threshold level

Count the boundaries to count the number of objects

Solution!

At 0.25 threshold At 0.5 threshold

At 0.6 thresholdAt 0.75 threshold

Snippet:

img=imread('F:\matlab sample images\color.png');img1=rgb2gray(img);Thresholdvalue=0.75;img2=im2bw(img1,Thresholdvalue);figure,imshow(img2); % to detect num of objectsB=bwboundaries(img2);num=length(B);

Drawing bounding box over objects

Snippet:

img=imread('F:\matlab sample images\color.png');img1=rgb2gray(img);Thresholdvalue=0.75;img2=im2bw(img1,Thresholdvalue);figure,imshow(img2); % to detect num of objectsB=bwboundaries(img2);num=length(B);

% to draw bow over objectsfigure,imshow(img2);hold on;for k=1:length(B), boundary = B{k}; plot(boundary(:,2), boundary(:,1), 'r','LineWidth',2);end

Image segmentation

Given an image of English alphabets, segment each and every alphabets

Perform basic morphological operations on the letters

Detect edges

Filter the noises if any

Replace the pixel with maximum value found in the defined pixel set (dilate)

Fill the holes in the images

Label every blob in the image

Draw the bounding box over each detected blob

output

Snippet:

a=imread('F:\matlab sample images\MYWORDS.png');im=rgb2gray(a);c=edge(im);se = strel('square',8); I= imdilate(c, se); img=imfill(I,'holes');figure,imshow(img); [Ilabel num] = bwlabel(img);disp(num);Iprops = regionprops(Ilabel);Ibox = [Iprops.BoundingBox]; Ibox = reshape(Ibox,[4 num]);imshow(I) hold on;for cnt = 1:num rectangle('position',Ibox(:,cnt),'edgecolor','r');end

Finding length of an object in an image

Algorithm:

Calibration:

Draw a line over the object of known length

Find the number of pixels contributing to the line

No of pixels in line =

Divide the known length in real world value by measured distance in pixel.

Distance per pixel= (Distance in real world unit)/ (no of pixels in line)

This gives value of single pixel to the value of real world unit

Algorithm:

Measurement:

Draw a line over the object of unknown length

Find the number of pixels contributing to the line

No of pixels in line =

Distance in real world units= (Distance per pixel)* (no of pixels in line)

Looking for code?

1. Write a program that solves the given equations calibration and measurement

Hint: for manual calculation, to get values of x1,x2,y1 and y2 use imtool in matlab

Simple image matching using edges

Algorithm:

Load two images to be matched

Detect edges of both images

Traverse through each pixel and count number of black and white points in one image (total value)

Compare value of each pixels of both the images (matched value)

Find the match percentage,

Match percentage= ((matched value)/total value)*100)

if match percentage exceeds certain threshold(say 90%), display, ‘image matches’

Input Image:

Output Image after edge detection:

Note: This method works for identical images and can be used for finger print and IRIS matching

Detecting washers/nuts in an image

From the given image, find the nuts and washers based on its features

Algorithm:

Analyze the image

Look for detectable features of nuts/washers

Preprocess the image to enhance the detectable feature

Hint - Use morphological operations

Create a detector to detect the feature

Mark the detected results

Detected washers

Mathematical operation on two functions f and g, producing a third function that is a modified version of one of the original functions

Example:

• Feature detection

Creating a convolution kernel for detecting edges:

• Analyze the logic to detect edges

• Choose a kernel with appropriate values to detect the lines

• Create a sliding window for the convolution kernel

• Slide the window through every pixel of the image

Convolution

Input image Output imageAfter convolution

FILTERING VERTICAL AND HORIZONTAL COMPONENTS

Algorithm:

• Load an image

• Create a kernel to detect horizontal edges• Eg:

• Find the transpose of the kernel to obtain the vertical edges

• Apply the kernels to the image to filter the horizontal and vertical components

Resultant image after applying horizontal filter kernel

Resultant image after applying vertical filter kernel

Snippet:

Using convolution:

rgb = imread('F:\matlab sample images\2.png');I = rgb2gray(rgb);imshow(I)hy = fspecial('sobel');hx = hy';hrFilt=conv2(I,hy);vrFilt=conv2(I,hx);

Using Fiters:

rgb = imread('F:\matlab sample images\2.png');I = rgb2gray(rgb);hy = fspecial('sobel');hx = hy';Iy = imfilter(double(I), hy, 'replicate');Ix = imfilter(double(I), hx, 'replicate');

Image preprocessing to detect/enhance features

Often includes,

Image color conversion

Histogram equalization

Edge detection

Morphological operations

Erode

Dilate

Open

Close

Example

To detect the required feature in an image,

• First subtract the unwanted features

• Enhance the required feature

• Create a detector to detect the feature

Gray scale

Histogram equalization

Edge detection:

Morphological close:

Image dilation:

OUTPUT

Detect the feature in the preprocessed image

IMAGE FUSION AND REGISTRATION

• Fusion: putting information together coming from different sources/data

• Registration: computing the geometrical transformation between two data

Applications:

• Medical Imaging

• Remote sensing

• Augmented Reality etc

Courtesy: G. Malandain, PhD, Senior Scientist, INRA

Courtesy: G. Malandain, PhD, Senior Scientist, INRA

BRAIN IMAGE REGISTERING

PET scan of Brain MRI scan of Brain

+ =

Output of multimodal registration( Different scanners)