An adaptive segmentation method using MRF model for suburban aerial images

Authors:
Mansuo Zhao;David Tien
Affiliations:
School of Accounting and Computer Science, Charles Sturt University, Bathurst, NSW, Australia.;School of Accounting and Computer Science, Charles Sturt University, Bathurst, NSW, Australia
Venue:
International Journal of Computer Applications in Technology
Year:
2009

Citing 6
Cited 0

A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Comparison of the Efficiency of Deterministic and Stochastic Algorithms for Visual Reconstruction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using a simple MRF model with a new implementation scheme

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

An adaptive image segmentation method using Markov random field model for suburban aerial images is presented in this paper. The image is modelled as a collection of regions characterised by slowly moving averages and standard deviation. Decreasing sized windows are used to calculate the moving averages during the iteration process. A function based weighting parameter between the two components in the energy function is also used to improve the performance of unsupervised segmentation. A hierarchical implementation scheme is also introduced to reduce the computation load and increase the segmentation speed.