Statistical multisensor image segmentation in complex wavelet domains

Authors:
Tao Wan;Zengchang Qin
Affiliations:
Robotics Institute, Carnegie Mellon University, Pittsburgh;Robotics Institute, Carnegie Mellon University, Pittsburgh and Intelligent Computing and Machine Learning Lab, School of ASEE, Beihang University, Beijing, China
Venue:
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Year:
2011

Citing 6
Cited 0

Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pixel- and region-based image fusion with complex wavelets

Information Fusion
Feature Extraction & Image Processing, Second Edition

Feature Extraction & Image Processing, Second Edition
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients

IEEE Transactions on Multimedia
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance

IEEE Transactions on Image Processing
Combined morphological-spectral unsupervised image segmentation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose an automated image segmentation algorithm for segmenting multisensor images, in which the texture features are extracted based on the wavelet transform and modeled by generalized Gaussian distribution (GGD). First, the image is roughly segmented into textured and non-textured regions in the dual-tree complex wavelet transform (DT-CWT) domain. A multiscale segmentation is then applied to the resulting regions according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring a Kullback-Leibler distance (KLD) between estimated GGD models for the neighboring segments. Experiments demonstrate that our algorithm achieves superior segmentation results.