A Critical Overview of Image Segmentation Techniques Based on Transition Region

A Critical Overview of Image Segmentation Techniques Based on Transition Region

Yu-Jin Zhang (Tsinghua University, China)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/978-1-5225-2255-3.ch112
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Image segmentation is the key step from image processing to image analysis, and is an important technique of image engineering. Image segmentation based on transition region is a special or distinctive type of techniques that are different from traditional boundary-based or region-based techniques. Since the first technique using transition region proposed, there are many subsequent related researches and applications, and a series of papers in the literature citing are published worldwide. Using Google Scholar, a number of papers citing the original papers are searched, a study on the statistics of these papers is conducted. These papers are sorted first according to the publishing year, and then grouped according to their purposes and contents (with techniques used). Some questionable issues in these papers are pointed out and critically discussed, and several further research directions are indicated and analyzed.
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The main contributions of the original journal papers (Zhang, 1991) and (Zhang, 1996) can be summarized into five points:

Key Terms in this Chapter

Clip Transform: One operation performed on the gray values of pixels. Classic clip transform assigns the maximum or minimum gray values to the cutting parts. In transition region determination based image segmentation, a particular clip transform is designed, which assigns the clip values to the cutting parts to avoid the effects caused by too big contrast at cutting edge, and to reduce the influence of various interferences.

Image Engineering (IE): An integrated discipline/subject comprising the study of all the different branches of image and video techniques. As a general term for all image techniques, it could be considered as a broad subject encompassing mathematics, physics, biology, physiology, psychology, electrical engineering, computer science, automation, etc. Its advances are also closely related to the development of telecommunications, biomedical engineering, remote sensing, document processing, industrial applications, etc.

Image Analysis (IA): One of three layers of image engineering, which is concerned with the extraction of information (by meaningful measurements with descriptive parameters) from an image (especially from interesting objects).

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.

Image: An entity that was captured by some visual systems in looking at the real world and that can be sensed to produce perception. It is a representation, likeness, or imitation of an object or thing, a vivid or graphic description, something introduced to represent something else.

Effective Average Gradient (EAG): A special expression for or a particular parameter of the average gradient of images, in which only those pixels with non-zero gradient values are involved in the computation. Since the influence of zero gradient pixels is removed, so it is called “effective”. It is a selective statistics of images, and the basis of transition region determination.

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

Image Techniques: A collection of various branches of techniques for processing (such as acquiring, capturing, sensing, storing, enhancing, filtering, debluring, inpainting, transforming, coding, transmitting, manipulating, etc .) analyzing (such as segmenting, representing, describing, featuring, measuring, classifying, recognizing), and understanding (such as modeling, registrating, matching, reconstructing, training, learning, reasoning, interpreting, etc .) images.

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