.png){kind=icon}
.png){kind=icon}
Return
A generalized multiclass histogram thresholding approach based on mixture modelling
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2013.09.004.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a new approach to multi-class thresholding-based segmentation. It considerably improves existing thresholding methods by efficiently modeling non-Gaussian and multi-modal class-conditional distributions using mixtures of generalized Gaussian distributions (MoGG). The proposed approach seamlessly: (1) extends the standard Otsu's method to arbitrary numbers of thresholds and (2) extends the Kittler and Illingworth minimum error thresholding to non-Gaussian and multi-modal class-conditional data. MoGGs enable efficient representation of heavy-tailed data and multi-modal histograms with flat or sharply shaped peaks. Experiments on synthetic data and real-world image segmentation show the performance of the proposed approach with comparison to recent state-of-the-art techniques. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Image segmentation
Multi-modal class thresholding
Mixture of generalized Gaussian distributions (MoGG)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W