Image Segmentation Research Paper

CHAPTER 1

INTRODUCTION

1.1 INTRODUCTION:

Image segmentation plays a vital role in Image Analysis and computer vision which is considered as the obstruction in the development of image processing technology, Image segmentation has been the subject of intensive research and a wide variety of segmentation techniques has been reported in the last two decades. Image segmentation is a classical and fundamental problem in computer vision. It refers to partitioning an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. As an integral step of many computer vision problems, the quality of segmentation output largely influences the performance of the whole vision system. In general terms, image segmentation

• Regions are grown iteratively by merging the neighboring pixels depending upon the merging criterion.

• This process is continued until all pixels are assigned to their respective regions as per merging criterion.

Region splitting:

Its principle is just opposite to region merging and whole image is continuously split until no further splitting of a region is possible.

Split and merge method:

This is the combination of splits and merges utilizing the advantage of the two methods. This method is based on quad quadrant tree representation of data whereby image segment is split into four quadrants provided the original segment is non-uniform in properties. After this the four neighboring squares are merged depending on the uniformity of the region (segments). This split and merge process is continued until no further split and merge is possible.

The limitation of region based segmentation is that there are chances of under segmentation and over segmentation of regions in the image. However, this problem can be rectified in two

Clustering is the task of identifying a finite set of categories (or clusters) to describe the data. Thus, similar objects are assigned to the same category and dissimilar ones to different categories. Clustering is also called unsupervised learning because the data objects are mapped to a set of clusters which can be interpreted as classes as well. Clustering is the process of grouping the data records into meaningful subclasses (clusters) in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters Other names for clustering are unsupervised learning (machine learning) and segmentation. Clustering is used to get an overview over a given data set Similarity between image regions or a pixel implies clustering (small separation distances) in the feature space. Clustering methods were some of the earliest data segmentation techniques to be