In this paper, invariant sonar texture characterization for seabed classification is addressed from the spatial distribution of image keypoints using log-Gaussian Cox processes. Considering the categorized visual keypoints, the spatial statistical properties of keypoint sets are expressed by the intensity and the pair correlation function of the log-Gaussian Cox model to define a novel invariant texture descriptor. Reported results of an application to sonar texture classification validate the proposed descriptor compared to previous work. We further discuss the main contribution of proposed approach, including the key features of a statistical model and complexity aspects.
['Huu-Giao Nguyen', 'Ronan Fablet', 'Jean-Marc Boucher']
Log-gaussian cox processes of visual keypoints for sonar texture recognition