The document discusses digital image representation and processing. It covers: 1) How digital images are represented as 2D arrays of integer pixel values stored in computer memory. 2) The main types of digital images - binary, grayscale, and true color images - based on the number of possible values per pixel. 3) Common image processing techniques like segmentation, thresholding, and histograms that analyze and modify digital images. 4) Thresholding converts pixels to black/white based on a threshold and is often used in segmentation. Histograms show pixel value distributions to aid analysis.

1. Vision system
(image processing)
By: karim ahmed abuamu

2. Image Representation
A digital image is a representation of a two-dimensional image as
a finite set of digital values, called picture elements or pixels
The image is stored in computer memory as 2D array of integers
Digital images can be created by a variety of input devices and
techniques:
 digital cameras,
 scanners,
 coordinate measuring machines etc.

3. Representation of Digital Images

4. Types of Images
Digital images can be classified according to number and nature
of those samples
 Binary
 Grayscale
 Color

5. Binary Images
A binary image is a digital image that has only two possible
values for each pixel
Binary images are also called bi-level or two-level
Binary images often arise in digital image processing as masks or
as the result of certain operations such as segmentation,
thresholding.

6. Grayscale Image Binary Image

7. Grayscale Images
A grayscale digital image is an image in which the value of each
pixel is a single sample.
Displayed images of this sort are typically composed of shades of
gray, varying from black at the weakest intensity to white at the
strongest.
The values of intensity image ranges from 0 to 255.

8. Grayscale Image

9. True color images
A true color image is stored as an m-by-n-by-3 data array that
defines red, green, and blue color components for each individual
pixel.
The RGB color space is commonly used in computer displays

10. True color Image

11. Image segmentation
In computer vision, image segmentation is the process of partitioning a
digital image into multiple segments (sets of pixels, also known as superpixels).
The goal of segmentation is to simplify and/or change the representation of an image
into something that is more meaningful and easier to analyze.
Image segmentation is typically used to locate objects and boundaries (lines, curves,
etc.) in images. More precisely, image segmentation is the process of assigning a
label to every pixel in an image such that pixels with the same label share certain
visual characteristics.

12. Histograms
o A tool that is used often in image analysis .
o The (intensity or brightness) histogram shows how many
times a particular grey level appears in an image.
o For example, 0 - black, 255 – white.
7
0 1 1 2 4 6
5
2 1 0 0 2 4
3
5 2 0 0 4 2
1
1 1 2 4 1 0
0 1 2 3 4 5 6
image histogram

13. Histogram Features
An image has low contrast when the complete range of possible
values is not used. Inspection of the histogram shows this
lack of contrast.

21. Histogram Equalization

23. Thresholding (image processing)
Threshold converts each pixel into black, white or unchanged depending on
whether the original color value is within the threshold range.
Thresholding is usually the first step in any segmentation
Single value thresholding can be given mathematically as follows:
1 if f ( x, y ) > T
g ( x, y ) = 
0 if f ( x, y ) ≤ T

24. Imagine a poker playing robot that needs to visually interpret the cards in
its hand
Original Image Thresholded Image

25. If you get the threshold wrong the results can be disastrous
Threshold Too Low Threshold Too High

26. Basic Global Thresholding
Based on the histogram of an image Partition the image histogram using a
single global threshold
The success of this technique very strongly depends on how well the histogram
can be partitioned
The basic global threshold, T, is calculated as follows:
1. Select an initial estimate for T (typically the average grey level in the
image)
2. Segment the image using T to produce two groups of pixels: G1
consisting of pixels with grey levels >T and G2 consisting pixels with grey
levels ≤ T
3. Compute the average grey levels of pixels in G1 to give μ1 and G2 to give
μ2

27. 4. Compute a new threshold value:
µ1 + µ 2
T=
2
5. Repeat steps 2 – 4 until the difference in T in successive iterations is
less than a predefined limit T∞
This algorithm works very well for finding thresholds when the histogram is suitable

28. Thresholding Example 1

29. Thresholding Example 2

30. Problems With Single Value Thresholding
Single value thresholding only works for bimodal histograms
Images with other kinds of histograms need more than a single threshold

31. Single value thresholding only works for bimodal histograms
Images with other kinds of histograms need more than a single threshold

32. Thank You