Abstract

In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.

Highlights

  • One of the most important tasks in image segmentation is to precisely extract objects from their backgrounds

  • Maximizing the factor2 means that the gray-level difference between Ca and Cb is tuned to the maximum by a proper threshold t1, which coincides with the principle of image segmentation

  • In the task of computer vision, it is of great importance to explore algorithms that

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Summary

Introduction

One of the most important tasks in image segmentation is to precisely extract objects from their backgrounds. From the viewpoint of information theory, the nonextensive parameter of an image can be determined by the maximization of the redundancy of the gray-level distribution [25]. The effective nonextensive parameter in the proposed algorithm is automatically determined by the information redundancy of an image [25].

Image Thresholding Algorithms
Otsu Algorithm
Otsu–Kapur Algorithm
Two-Dimensional Entropic Algorithm
Tsallis Entropy Algorithm
New Algorithm
Objective functions of the
Results
Experimental Results
Conclusions
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