Theoretical Concepts and Technical Aspects on Image Segmentation

Theoretical Concepts and Technical Aspects on Image Segmentation

Anju Pankaj (Mahatma Gandhi University, India) and Sonal Ayyappan (SCMS School of Engineering and Technology, India)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/978-1-5225-0498-6.ch002
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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 image. Each of the pixels in a region is similar with respect to some characteristic or computed property. Adjacent regions are significantly different 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 defined. 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 different methods in image segmentation and discusses a case study on it.
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Background

Graph-based image segmentation techniques generally represent the problem in terms of a graph, G =(V,E) where each node vi Ɛ V corresponds to a pixel inthe image, and the edges in E connect certain pairs of neighboring pixels. A weight is associated with each edge based on some property of the pixels that it connects, such as their image intensities. Depending on the method, there may or may not be an edge connecting each pair of vertices. The earliest graph based methods use fixed thresholds and local measures in computing segmentation.

Different cluster-structures can be detected using algorithms on minimum spanning tree. The dataset used by Zahn include Fisher Iris data in four dimensional space. The work of Zahn (1971) presents a segmentation method based on the minimum spanning tree (MST) of the graph. This method has been applied both to point clustering and to image segmentation. For image segmentation the edge weights in the graph are based on the differences between pixel intensities, whereas for point clustering the weights are based on distances between points. The segmentation criterion in Zahn’s method is to break MST edges with large weights, which is inadequate. Differences between pixels within the high variability region can be larger than those between the ramp and the constant region. Thus, depending on the threshold, simply breaking large weight edges would either result in the high variability region being split into multiple regions, or would merge the ramp and the constant region together.

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