A Non-extensive Entropy-Based Adaptive Multi-threshold Image Segmentation Algorithm

Image segmentation plays important role in computer vision and image processing, and the threshold segmentation is known by its powerful and efficient performance. Entropy-based algorithm and variance-based algorithm are two main threshold segmentation algorithms. In present paper, non- extensive Ts...

Full description

Saved in:
Bibliographic Details
Published in:2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI) pp. 838 - 844
Main Authors: Kou, Qiaoying, Xiong, Jing, Sun, Mingjie, Ou, Congjie
Format: Conference Proceeding
Language:English
Published: IEEE 19.08.2022
Subjects:
Computer vision
Correlation
Entropy
gray-level histogram
Histograms
Image segmentation
multi-level thresholding
non-extensive parameter
PSNR
self-adaptive algorithm
Thresholding (Imaging)
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image segmentation plays important role in computer vision and image processing, and the threshold segmentation is known by its powerful and efficient performance. Entropy-based algorithm and variance-based algorithm are two main threshold segmentation algorithms. In present paper, non- extensive Tsallis entropy is adopted to multi-level image thresholding. The non-extensive parameter is determined by the gray-level histogram in a self-adaptive way, which indicates the long-range correlations among pixels in an image. The segmentation results show that the proposed algorithm is superior to the well-known Kapur multi-level thresholding and variance- based Otsu algorithm in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and uniformity measure. Furthermore, it is found that the superiority of the proposed algorithm becomes stable as the number of thresholds increases.
DOI:10.1109/PRAI55851.2022.9904107