Image Processing

From TSirJi

Definition

Image Processing is an area of scientific experimental work, done on images (RGB or Gray Scale) to propose a solution to a given problem. Basically developing a brain (computer) that can learn and train himself to perform tasks using eyes (camera). It means minimum or no use of electronic sensors.

What is Digital Image Processing ?

Any digital image can be defined as a two-dimensional function, f(x,y), where x & y are spatial coordinates. The amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point.
We can call an image a digital image if amplitude values of f and values of x & y are all finite, discrete quantities.

For Example:

  • x = 0, 1, 2, .... 236 and y = 0, 1, 2, .... 155 and
f(x,y) = 2xy;
f(0,0) = 0;
f(0,1) = 0;
f(1,0) = 0;
The above function f(x,y) can't be considered a digital image function, although it's producing finite values but they are not discrete.
f(x,y) = 2x+y;
f(0,0) = 0;
f(0,1) = 1;
f(1,0) = 2;
The function f(x,y) = 2x+y; is an example which is producing finite as well as discrete values.

Every digital image is composed of a finite number of elements. Each of these finite elements has a particular location and value. These elements can be referred to as pixels (the term used most widely) or picture elements or image elements or pels. In Digital Image Processing, digital images are processed by computers to gain output as desired by algorithms.
Images contain more information then text. Vision is the most advanced of human senses and it plays a vital role in human perception. We humans are limited to visual band of electromagnetic spectrum but imaging machines are not, they can cover almost an entire electromagnetic spectrum from gamma waves to radio waves.
Machines can operate on images generated from ultrasound, electron microscopy, infrared and computer-generated images but humans can't ! Thus digital image processing has a wide & varied field of application. There are no pre-defined boundaries where you can say image processing ends here and other related areas (such as computer vision) begins there. There is a thin line of difference where we can say who is who.
In Image Processing both I/P's and O/P's are images.
In Computer Vision it's ultimate goal is to use computational power to emulate (copy or mimic or mirror) human vision.
If you are trying to emulate human intelligence using computers, you call it Artificial Intelligence.
The area of image understanding or image analysis comes between image processing and computer vision.
A useful paradigm in this continuum is to consider computerized processes in three categories.

Low-Level Mid-level High-Level
  • Involves primitive operations such as image sharpening, noise reduction, contrast enhancement
  • Tasks such as image segmentation, partitioning an image into regions)
  • I/P's are whole images but O/P's are attributes from images such as edges, identity of an individual object, contours
  • "Making Sense" by recognizing objects
  • Building vision associated with human vision

Topics

Matlab Functions and Practical Topics Theoretical Topics
File Path, Extension & Output Format Matlab

Coordinate System

clear, CLC command Matlab

Classes & Image Types Matlab

edit and edit filename command Matlab

M_function_programming_Matlab

imread() Matlab

(.) dot notation Matlab

imshow() Matlab

Operators (Arithmetic, Relational & Logical) and Flow control statements Matlab

figure Matlab

Array Manipulation Matlab

imwrite() Matlab

Function handles Matlab

(.') transpose Matlab

Cell Arrays Matlab

(:) colon Matlab

Structures Matlab

end Matlab

Optimizing Program Code Matlab

Accessing specific elements of an array Matlab

Spatial Domain Processing Matlab

Accessing elements of an array with fixed gap upto end Matlab Neighborhood Processing Matlab
Sum of a 2D Matrix Matlab Intensity Transformation Matlab
Logical Indexing of Array Matlab Monochrome Images Matlab
Linear Indexing of Array Matlab
im2bw() Matlab
imbinarize() Matlab
im2double() Matlab
size() Matlab
uint16() Matlab
mean() mean2() Matlab
numel() Matlab
tic and toc Matlab
timeit() Matlab
zeros() Matlab
meshgrid() Matlab
logical() Matlab
islogical() Matlab
rgb2gray() Matlab
rgb2ind() Matlab
imagesc() Matlab
colormap() Matlab
colorbar() Matlab
surf() Matlab
plot() Matlab
rgb2hsv() Matlab
imadjust() Matlab
stretchlim() Matlab
interp1() Matlab
linspace() Matlab
im2uint8() Matlab
mat2gray() Matlab