what is an image.? How to represent the image in matlab

1. IMAGE REPRESENTATION
Dr. S. SHAJUN NISHA, M.C.A., M.Phil., M.Tech., M.B.A., Ph.D.,
Assistant Professor and Head,
PG and Research Department of Computer Science,
Sadakathullah Appa College,
Tirunelveli- 627011.

2. Digital Images
• 2D array of numbers – representing
intensity.
• Numbers stored as bits.
• Generally used – 8 bit/pixel for Gray Scale
Images, 24 bit/pixel for Color
Images.

3. What is a digital image?
The image can now be represented as a matrix
of integer values

4. How are images represented
in the computer?

5. Color images

6. Digital Image Representation
• An image may be defined as a two-dimensional function f
(x,y), where x and y are spatial (plane) coordinates, and the
amplitude of f at any pair of coordinates is called the
intensity of the image at that point.
• The term gray level is used often to refer to the intensity of
monochrome images. Color images are formed by a
combination of individual images.
• For example, in the RGB color system a color image consists
of three individual monochrome images, referred to as the red
(R), green (G), and blue (B) primary (or component) images.

7. Contd..
• An image may be continuous with respect to the x- and y-
coordinates, and also in amplitude.
• Converting such an image to digital form requires that the
coordinates, as well as the amplitude, be digitized.
• Digitizing the coordinate values is called sampling;
• Digitizing the amplitude values is called quantization.
• Thus, when x, y, and the amplitude values of f are all
finite, discrete quantities, we call the image a digital
image.

8. Image processing
 An image processing operation typically defines a new
image g in terms of an existing image f.
 We can transform either the range of f.
 Or the domain of f:
 What kinds of operations can each perform?

9. Image digitization
 Sampling means measuring the value of an image at a finite number of points.
 Quantization is the representation of the measured value at the sampled point by
an integer.

10. Image quantization(example)
• 256 gray levels (8bits/pixel) 32 gray levels (5 bits/pixel) 16 gray levels (4 bits/pixel)
• 8 gray levels (3 bits/pixel) 4 gray levels (2 bits/pixel) 2 gray levels (1 bit/pixel)

11. Image sampling (example)
original image sampled by a factor of 2
sampled by a factor of 4 sampled by a factor of 8

12. Coordinate Conventions

13. Images as Matrices
The right side of this equation is a digital image by definition. Each element of
this array is called an image element, picture element, pixel, or pel. The terms
image and pixel are used throughout the rest of our discussions to denote a
digital image and its elements.
A digital image can be represented as a MATLAB matrix:

14. Classes

15. Image Types
• The toolbox supports four types of images:
Gray-scale images
Binary images
Indexed images
RGB images
• Most monochrome image processing operations are carried out
using binary or gray-scale images, so our initial focus is on
these two image types.

16. Gray-scale Images:
• A gray-scale image is a data matrix whose values
represent shades of gray.
• When the elements of a gray-scale image are of class
uint8 or uint16, they have integer values in the range
[0, 255] or [0, 65535], respectively.
• If the image is of class double or single, the values
are floating-point numbers (see the first two entries in
Table 2.3).
• Values of double and single gray-scale images

17. Binary images
• A binary image is a logical array of 0s and 1s. Thus, an array
of 0s and 1s whose values are of data class, say, uint8, is not
considered a binary image in MATLAB.
• A numeric array is converted to binary using function logical.
Thus, if A is a numeric array consisting of 0s and 1s, we create
a logical array B using the statement
B = logical(A)
• If A contains elements other than 0s and 1s, the logical
function converts all nonzero quantities to logical 1s and all
entries with value 0 to logical 0s.

18. • To test if an array is of class logical we use the islogical
function:
islogical(C)
• If C is a logical array, this function returns a 1. Otherwise it
returns a 0.
• Logical arrays can be converted to numeric arrays using the
class conversion functions

19. Class Conversion Functions
• Converting images from one class to another is a common
operation. When converting between classes, keep in mind the
value ranges of the classes being converted.
• The general syntax for class conversion is
B = class_name(A)
• where class_name refers to double, single, uint8 etc…
• The toolbox provides specific functions that perform the
scaling and other bookkeeping necessary to convert images
from one class to another.

20. f =
− 0.5 0.5
0.75 1.5
Performing the conversion
>> g = im2uint8(f)
yields the result
g =
0 128

21. What is DIP? (cont…)
•The continuum from image processing to
computer vision can be broken up into low-,
mid- and high-level processesLow Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation
High Level Process
Input: Attributes
Output: Understanding
Examples: Scene
understanding,
autonomous navigation
In this course we will
stop here

22. IP Types
IMAGE in IMAGE out
• Restoration
• Enhancement
• Compresion
IMAGE in ATTRIBUTES out
• Edge Detection
• Contours
• Segmentation
• Classification
• Identifying individual objects
HIGH LEVEL
•Image Analysis

23. Challenges(Tasks) in Image Processing
 Image Enhancement
 Image Restoration(Noise Removal)
 Image Segmentation
 Image Classification
 Edge Detection
 Shape Representation and Description
 Object Recognition
 Image Understanding – retrieving required information from an image.
 Data Compression
 Motion Analysis
 Texture Analysis
 Etc

24. Towards image understanding
john@hulskamp.com.au 24
• Computer Vision: making useful decisions about real physical objects and scenes based on sensed images
• To get to that understanding we need to process images: hence Image Processing is a step towards this
understanding
• Image Processing issues:
– Image Statistics & Histograms
– Image Enhancement
– Image Restoration
– Image Analysis: Edge Detection & Feature extraction
– Representation & Description
– Pattern Recognition

25. IP Domains
Spatial (Time) Domain
Analysis
Frequency Domain
Analysis
Wavelet And
Multiresolution
Process
Morphological IP
1. Image Enhancement
2. Image Restoration
3. Image Compression
4. Image Segmentation
1. Image Matching
2. Registration
3. Edge Detection
4. etc
1. Edge Detection
2. Region Filling
3. Thinning
4. Thickening
5. Pruning
6. etc