Half Century for Image Segmentation

Half Century for Image Segmentation

Yu-Jin Zhang (Department of Electronic Engineering, Tsinghua University, China)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/978-1-4666-5888-2.ch584
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Image (and video) segmentation is an important image technique, and is often described as the process that subdivides an image (or a clip of frames) into its constituent parts and extracts those parts of interest (objects). It is well known by its utility, since for extracting the useful information from images or a group/sequence of images, to separate the objects from background is an inevitable step/task. It is also well known by its complexity, as there is no general theory for image segmentation, yet. Therefore, the development of image segmentation techniques is still an ad hoc process.

Image segmentation is one of the most critical tasks in automatic image analysis, which is at the middle layer of image engineering (IE). Image Engineering (which is composed of three layers from bottom to top: image processing, image analysis and image understanding) is a new discipline and a general framework for all image techniques (Zhang, 2008d).

According to the statistics gathered from a yearly bibliography survey on image engineering (Zhang 2013), the journal publication on image segmentation is ranked the first among the current 16 groups/branches of research techniques of image engineering. The comprehensive survey has been made consecutively for 18 years, and the totally involved papers are more than 40000, in which 8243 are related to the different technique groups of image engineering. The statistics for the distribution of these papers in each group are listed in Table 1. It is seen that the group of image segmentation is the one that attracts the most attentions and achieve the most results among a complete list of technique groups.

Table 1.
Journal papers in different technique groups
NoTechnique Group# of PapersNoTechnique Group# of Papers
1Segmentation and edge detection12389Content-based image retrieval347
2Enhancement and filtering97410Reconstruction from projections303
3Coding / decoding89611Analysis and feature measurement287
4Object extraction and recognition83212Object representation and description233
5Registration, matching and fusion810133-D modeling and scene recovery231
6Biometrics64314Multiple-resolution processing158
7Watermarking, and information hiding59915Spatial-temporal technology90
8Capturing and storage52316Image perception and interpretation79

Key Terms in this Chapter

Region Growing: Region growing is a region-based sequential technique for image segmentation by assembling pixels into larger regions based on predefined seed pixels, growing criteria and stop conditions.

Image Segmentation: A process consists of subdividing an image into its constituent parts and extracting these parts of interest (objects) from the image.

Graph Search: Graph search is a particular type of segmentation techniques which combing edge detection and linking together. It represents edge segments in the form of a graph and searching the graph for low-cost paths that correspond to significant edges or boundaries of objects.

Watersheds: Watershed technique is inspired from the topographic interpretation of images Segmentation by watersheds embodies many concepts of edge detection, thresholding and region processing techniques, and often produces stable and continuous results.

Clustering: Clustering is also called unsupervised learning and is a powerful technique for pattern classification. It is a process to group, based on some defined criteria, two or more terms together to form a large collection. In the context of image segmentation, it is often considered as the multi-dimensional extension of the thresholding technique.

Edge Detection: Edge detection is the most common approach for detecting discontinuities in images, and is the fundamental step in edge-based parallel process for segmentation. An edge is a local concept. To form a complete boundary of an object, edge detection should be followed by edge linking or connection.

Gradient Operator: Gradient operator is the first type of operators used for edge detection. The gradient of an image is a vector consisting of the first-order derivatives (including the magnitude and direction) of an image.

Thresholding: Thresholding techniques are the most popularly used segmentation techniques. A set of suitable thresholds need to be first determined, and then the image can be segmented by comparing the pixel properties with these thresholds.

Active Contour Model: Active contour model is a sequential technique for image segmentation. Given an approximation of the boundary of an object in an image, an active contour model can be used to find the “actual” boundary by deforming the initial boundary to lock onto features of interest within in this image.

Image Engineering: An integrated discipline/subject comprising the study of all the different branches of image and video techniques. It mainly consists of three levels: Image Processing, Image analysis, Image understanding.

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