Open AccessTextural lacunarity for semi‐supervised detection in sonar imagery
Author(s) -
Nelson James Daniel Bryan,
Krylov Vladimir
Publication year - 2014
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2013.0226
Subject(s) - lacunarity , artificial intelligence , sonar , synthetic aperture sonar , pattern recognition (psychology) , robustness (evolution) , wavelet , computer science , computer vision , fractal , wavelet transform , histogram , fractal dimension , mathematics , image (mathematics) , mathematical analysis , biochemistry , chemistry , gene
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.