A new method is proposed to integrate 3D optical and acoustic images relative to the same underwater environment. The combination of optical and acoustic sensors in terms of uniform reference system, geo-referencing and time allows: (i) integration cascade (operational level), (ii) safety data acquisition in various domains (distance from ground, turbid water, vegetation, etc.), (iii) replanning of missions in progress. Furthermore, data fusion can be faced according to different approaches: (a) stratification of referenced data layers, (b) correlation of quantities of different nature, (c) comparison of extracted features: 2D geometries (segments, elementary curves) and 3D (planes, simple surfaces), repetitive patterns, (d) integration of semantic information, (e) template matching for recognizing known structures, (f) creation and refinement of probability maps as a measure of optical (geometry, texture) and acoustic (elevation or reflectivity maps) properties. A set of geometrical and textural feature extraction algorithms is applied to the multi-sensor images and the output results are compared. We aim thus at emphasizing the geometric features correspondences (e.g., lines or different kind of curves), instead of descriptor-based individual feature matching.
Underwater scene understanding by optical and acoustic data integration
Moroni D;Pascali MA;Reggiannini M;Salvetti O
2013
Abstract
A new method is proposed to integrate 3D optical and acoustic images relative to the same underwater environment. The combination of optical and acoustic sensors in terms of uniform reference system, geo-referencing and time allows: (i) integration cascade (operational level), (ii) safety data acquisition in various domains (distance from ground, turbid water, vegetation, etc.), (iii) replanning of missions in progress. Furthermore, data fusion can be faced according to different approaches: (a) stratification of referenced data layers, (b) correlation of quantities of different nature, (c) comparison of extracted features: 2D geometries (segments, elementary curves) and 3D (planes, simple surfaces), repetitive patterns, (d) integration of semantic information, (e) template matching for recognizing known structures, (f) creation and refinement of probability maps as a measure of optical (geometry, texture) and acoustic (elevation or reflectivity maps) properties. A set of geometrical and textural feature extraction algorithms is applied to the multi-sensor images and the output results are compared. We aim thus at emphasizing the geometric features correspondences (e.g., lines or different kind of curves), instead of descriptor-based individual feature matching.File | Dimensione | Formato | |
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