NDLI: Textural lacunarity for semi-supervised detection in sonar imagery

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Textural lacunarity for semi-supervised detection in sonar imagery

Content Provider	IET Digital Library
Author	Nelson, James Daniel Bryan Krylov, Vladimir
Abstract	Wavelet energy-based lacunarity features, which measure deviations from translational statistical invariance over multiple scales, were recently proposed for object detection and classification in sonar imagery. The authors here extend the idea to incorporate further robustness to background type whilst retaining sensitivity to local changes in texture caused by the presence of man-made objects. The resulting textural-lacunarity features are constructed by estimating the joint distribution of local neighbourhoods with empirical distributions over an adaptive texton dictionary. Experiments on a synthetic aperture sonar imagery dataset suggest that the features offer significant improvements in the receiver operating curve.
Starting Page	616
Ending Page	621
Page Count	6
ISSN	17518784
Volume Number	8
e-ISSN	17518792
Issue Number	Issue 6, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/8/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2013.0226
Journal	IET Radar, Sonar & Navigation
Publisher Date	2014-01-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Texton Dictionary Computer Vision And Image Processing Technique Empirical Distribution Image Classification Image Texture Joint Distribution Local Neighbourhoods Man Made Objects Object Classification Object Detection Optical, Image And Video Signal Processing Receiver Operating Curve Semisupervised Detection Sonar And Acoustic Radar Sonar Imaging Synthetic Aperture Sonar Synthetic Aperture Sonar Imagery Dataset Textural Lacunarity Translational Statistical Invariance Wavelet Energy Based Lacunarity Feature
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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