Image Segmentation Technique and Its Application
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Image segmentation is an important issue and it's also a classical problem.In this paper three kinds of image segmentation technology are intruduced.They are threshold segmentation method,Edge detection method and region segmentation method.New technology of Image segmentation is also described.Watershed algorithm based on tag is applied in this paper.From the simulation results we conclude that marker selection is the key to image segmentation algorithm.Keywords:
Segmentation-based object categorization
Region growing
Range segmentation
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Understanding of images may become a boon in e-commerce industry. By identifying the images one may find multiple applications which are useful to compare its various features and use them to influence anyone as a customer or retailer by avoiding delicacy of the product information. In identifying an image image segmentation has become a pronounced tool. Global and local image segmentation method have recently been used by many researchers including medical, sports, agriculture and many other fields. This paper addresses the overview of image segmentation techniques and provides the extensive information of different type of image segmentation methods in brief. Methods such as edge based segmentation, Fuzzy segmentation, Threshold segmentation, PDE based segmentation, ANN based segmentation, Region based segmentation, Water shed based segmentation Cluster ensemble based segmentation, image regimentation and fast trust region for segmentation have been explained with examples.
Segmentation-based object categorization
Region growing
Range segmentation
Market Segmentation
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Thousands of image segmentation algorithms have been proposed so far, and new algorithms are proposed continuously. Image segmentation can be classified by the view of image segmentation application, or by the theoretical tool used in image segmentation. The main research fields including Otsu method, clustering, motion segmentation, image segmentation method based on graph theory and image segmentation method based on active contour. Natural computing algorithm has become a new hotspot of image segmentation.
Segmentation-based object categorization
Region growing
Range segmentation
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Image segmentation is a key step in image processing and computer vision,it is also the foundation of image understanding.Recently image segmentation is valued as an important topic according to its significance at image processing domain.This paper systematically introduces theories foundation and application of image segmentation from the traditional and innovative points.
Segmentation-based object categorization
Foundation (evidence)
Region growing
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Medical image plays an important role in the assist doctors in the diagnosis and treatment of diseases. For the medical image, the further analysis and diagnosis of the target area is based on image segmentation. There are many different kinds of image segmentation algorithms. In this paper, image segmentation algorithms are divided into classical image segmentation algorithms and segmentation methods combined with certain mathematical tools, including threshold segmentation methods, image segmentation algorithms based on the edge, image segmentation algorithms based on the region, image segmentation algorithms based on artificial neural network technology, image segmentation algorithms based on contour model and image segmentation algorithm based on statistical major segmentation algorithm and so on. Finally, the development trend of medical image segmentation algorithms is discussed.
Segmentation-based object categorization
Region growing
Range segmentation
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Image segmentation,an important image analysis technique,is designed to segment image into featured sections and take out the desired parts. The result of image segmentation is the basis of image feature extraction and identification. This article presents such image segmentation methods as threshold segmentation and edge detection. The segmentation computation experiment was conducted so as to determine the advantages and demerits of these methods to provide reference for the application of these methods.
Segmentation-based object categorization
Morphological gradient
Region growing
Range segmentation
Feature (linguistics)
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Segmentation-based object categorization
Region growing
Connected-component labeling
Range segmentation
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Citations (2)