09-11-2012, 05:34 PM
Image Segmentation
Image Segmentation.ppt (Size: 2.01 MB / Downloads: 77)
Introduction
The purpose of image segmentation is to partition an image into meaningful regions with respect to a particular application
The segmentation is based on measurements taken from the image and might be greylevel, colour, texture, depth or motion
Usually image segmentation is an initial and vital step in a series of processes aimed at overall image understanding
Applications of image segmentation include
Identifying objects in a scene for object-based measurements such as size and shape
Identifying objects in a moving scene for object-based video compression (MPEG4)
Identifying objects which are at different distances from a sensor using depth measurements from a laser range finder enabling path planning for a mobile robots
Greylevel histogram-based segmentation
We will look at two very simple image segmentation techniques that are based on the greylevel histogram of an image
Thresholding
Clustering
We will use a very simple object-background test image
We will consider a zero, low and high noise image
How do we characterise low noise and high noise?
We can consider the histograms of our images
For the noise free image, its simply two spikes at i=100, i=150
For the low noise image, there are two clear peaks centred on i=100, i=150
For the high noise image, there is a single peak – two greylevel populations corresponding to object and background have merged
Greylevel thresholding
We can easily understand segmentation based on thresholding by looking at the histogram of the low noise object/background image
There is a clear ‘valley’ between to two peaks
Any threshold separates the histogram into 2 groups with each group having its own statistics (mean, variance)
The homogeneity of each group is measured by the within group variance
The optimum threshold is that threshold which minimizes the within group variance thus maximizing the homogeneity of each group
Greylevel clustering
But, we have a chicken and egg situation
The problem with the above definition is that each group mean is defined in terms of the partitions and vice versa
The solution is to define an iterative algorithm and worry about the convergence of the algorithm later