Image segmentation by automatic histogram thresholding

Authors:
Ruey-Ming Chao;Hsien-Chu Wu;Zi-Chun Chen
Affiliations:
National United University, Miaoli, Taiwan, R.O.C.;National Taichung Institute of Technology, Taichung, Taiwan, R.O.C.;National Taichung Institute of Technology, Taichung, Taiwan, R.O.C.
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

Citing 13
Cited 2

On coresets for k-means and k-median clustering

STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Image segmentation by histogram thresholding using hierarchical cluster analysis

Pattern Recognition Letters
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm

Pattern Recognition Letters
A double-threshold image binarization method based on edge detector

Pattern Recognition
A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

Computer Vision and Image Understanding
Edge detection improvement by ant colony optimization

Pattern Recognition Letters
Unimodal thresholding for edge detection

Pattern Recognition
An artificial ant colonies approach to medical image segmentation

Computer Methods and Programs in Biomedicine
Optimal multi-level thresholding using a two-stage Otsu optimization approach

Pattern Recognition Letters
New neutrosophic approach to image segmentation

Pattern Recognition
Histogram thresholding using fuzzy and rough measures of association error

IEEE Transactions on Image Processing
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation

IEEE Transactions on Image Processing
Novel Noncontrast-Based Edge Descriptor for Image Segmentation

IEEE Transactions on Circuits and Systems for Video Technology

Exploiting HPC resources for the 3D-time series analysis of caries lesion activity

Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
Exploiting MapReduce and data compression for data-intensive applications

Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

Thresholding is an important technology for image segmentation. Before we get the segmentation thresholds, most segmentation technologies need to set many parameters. This paper presents a method to automatically determine how many thresholds should be set and what the best range of each threshold is for different images. It finds the segmentation threshold by observing the change of the variance values and the mean values of each threshold range in the image histogram. The proposed method needs simple calculation, so that it has less time complexity.