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Chapter 2
DOI: 10.4018/978-1-5225-0498-6.ch002
ABSTRACT
Image segmentation is the process of partitioning a digital image into multiple segments (super pixels).
Segmentation is typically used to locate objects and boundaries in images. The result of segmentation
is a set of segments that collectively cover the entire image, or a set of contours extracted from the im-
age. Each of the pixels in a region is similar with respect to some characteristic or computed property.
Adjacent regions are signifcantly diferent with respect to the same characteristics. A predicate for
measuring the evidence for a boundary between two regions using a graph-based representation of
the image is defned. An important characteristic of the method is its ability to preserve detail in low-
variability image regions and ignoring detail in high variability regions. This chapter discuss basic
aspects of segmentation and an application and presents a detailed assessment on diferent methods in
image segmentation and discusses a case study on it.
INTRODUCTION
Image segmentation is a very significant area in computer vision. Image segmentation, partitions an
image into multiple regions based on certain similarity constraints. This acts as the pre-processing stage
in several image analysis problems like image compression, image recognition etc. Segmentation is the
vital part for the successful extraction of image features and classification. Image segmentation can be
defined as the partition of an image into several regions or categories. These regions can be similar in
any features like color, texture, intensity etc. Every pixel in an image is assigned to any one of the cat-
egorised region. Quality of segmentation is described as pixels in the same region are similar in some
Theoretical Concepts and
Technical Aspects on
Image Segmentation
Anju Pankaj
Mahatma Gandhi University, India
Sonal Ayyappan
SCMS School of Engineering and Technology, India