1. Machine Vision
ENT 273
Hema C.R.
Binary Image Processing
Lecture 3

2. Road Map Monochrome Images Binary Image Binary Image Processing
Binary Algorithms
Hema ENT 273 Lecture 3

3. Monochrome Images
Monochrome images consist of one color [shades of gray values] also called gray level images
Information in these images is represented as gray values from 0 to 255 or 256 levels
Monochrome images were used in the early days of machine vision when computing power were less.
Most machine vision applications use monochrome images to reduce processing time
Hema ENT 273 Lecture 3

4. Grayscale Image Also known as intensity images
Image is represented as a matrix with each pixel assigned gray intensity level. Two types
Data type [double class] - a floating number between 0 and 1 is assigned to the pixels [many mathematical functions can only be applied to double class]
Unit 8 - an integer between 0 and 255 is assigned to the pixels. [requires only 1/8 storage compared to double class]
Hema ENT 273 Lecture 3

5. Binary Images
Binary images are images whose pixels have only two possible intensities 0 or 1
Binary images contains only two colors, black[0] and white[1or 255]
Requires significantly smaller memory and processing requirements
Well developed algorithms are available for binary imaging
Ideal for industrial applications which require only silhouette of objects
Hema ENT 273 Lecture 3

6. Formation of Binary Images
Binary images are formed by thresholding a grayscale or color image to separate an object from the background
Thresholding
Is a method to convert a grayscale image to a binary image so that objects are separated from the background
Threshold
Hema ENT 273 Lecture 3

7. Thresholding
For thresholding to be effective objects and backgrounds should have sufficient contrast.
Intensity values of objects or background must be known
Fixed threshold
Range between T1 and T2
General threshold
Z is a set of intensity values for object components
Same threshold values may not work for new domains
Threshold is selected on application basis
Automatic thresholding is the first step in analysis of images in machine vision systems
Hema ENT 273 Lecture 3

8. Geometric Properties of Binary Images8 4
Connectivity
A pixel is connected to another pixel if and only if there exists a path between them
4 connected -4 immediate neighbors
8 connected corner neighbors
Connected components - set of connected pixels
Background - set of connected 0 pixels 1 2 3 4
Hema ENT 273 Lecture 3

9. Geometric Properties of Binary Images
Size or area of binary image is
Position
Position of a binary is determined by finding center of the image or centroid C[x,y]
Hema ENT 273 Lecture 3

10. Geometric Properties of Binary Images
Horizontal projection
Image
Projections
Projections are compact representations of images
Projections are not unique more than one image can have the same projections
Figure shows the horizontal [H] and vertical [V] projections of an image
Vertical projection
Hema ENT 273 Lecture 3

11. Binary images Definitions Neighbors Path Foreground Connectivity
4 neighbors [ 4 connected], 8 neighbors [8 connected],
Path
Sequence of pixel indices
Foreground
Set of all 1 pixels in an image
Connectivity
Pixels are said to be connected if there exists a path between them
Connected components
Set of pixels in which each pixel is connected to all other pixels
Background
Set of connected pixels that have points on the border of an image
Boundary
Set of pixels that are at the outer edge of the image
Interior
Set of pixels that are not in the boundary
Surrounds
Pixels that surround all image pixels
Hema ENT 273 Lecture 3

12. Binary Algorithms Component Labeling Size Filter
Component labeling algorithm finds all connected components in an image and assigns a unique label to all points in the same component.
Size Filter
Size filters are used to remove noise, this filters removes all regions smaller than a given threshold.
Poor choice of threshold results in errors 3 1 2
Hema ENT 273 Lecture 3

13. Binary Algorithms Euler Number Region Boundary
Genus or Euler number is defined as the number of connected components minus the number of holes
E = C – H
Region Boundary
The algorithms selects a starting pixel and tracks the boundary until it comes back to the starting pixel
Hema ENT 273 Lecture 3

14. Binary Algorithms Area Area is the number of pixels in a component
Perimeter
The number of boundary pixels of a component
Hema ENT 273 Lecture 3

15. Holes
Perimeter
Hema ENT 273 Lecture 3

16. Morphological Operators
Thinning
Binary regions are reduced to lines that approximate their center lines
Purpose of thinning is to reduce components to their essential information to facilitate further analysis
Dilation
This operator enlarges the boundary of foreground pixels, holes inside pixels become smaller
Erosion
Erodes away the boundary regions, foreground pixels shrink in size 16
Hema ENT 273 Lecture 3

17. Morphological Operators
Close
Closing is similar in some ways to dilation in that it tends to enlarge the boundaries of foreground (bright) regions in an image
Open
The basic effect of an open operator is somewhat like erosion in that it tends to remove some of the foreground (bright) pixels from the edges of regions of foreground pixels. However it is less destructive than erosion in general
Thickening
Thickening is a morphological operation that is used to grow selected regions of foreground pixels in binary images.
Hema ENT 273 Lecture 3

18. References http://homepages.inf.ed.ac.uk/rbf/HIPR2/label.htm
Hema ENT 273 Lecture 3

19. Machine Vision
Binary Image Processing
End of lecture 3