Three-dimensional object recognition
ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Physical modeling and combination of range and intensity edge data
CVGIP: Image Understanding
Computer Vision and Image Understanding
A Probabilistic Approach to the Coupled Reconstruction and Restoration of Underwater Acoustic Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sonar Signal Processing
Bayesian Modeling of Uncertainty in Low-Level Vision
Bayesian Modeling of Uncertainty in Low-Level Vision
Three-Dimensional Object Recognition Systems
Three-Dimensional Object Recognition Systems
Segmentation of Multibeam Acoustic Imagery in the Exploration of the Deep Sea-Bottom
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
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This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired data. Beamforming, a method widely applied in acoustic imaging, is used to build a three-dimensional image, associated point by point with another kind of information representing the reliability (i.e., "confidence") of such an image. Unfortunately, this kind of images is plagued by several problems due to the nature of the signal and to the related sensing system, thus heavily affecting data quality. Specifically, speckle noise and the broad directivity characteristic of the sensor lead to very degraded images. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields whose associated probability distributions are specified by a single energy functional. A threefold process has been applied able to reconstruct, segment, and restore the involved acoustic images exploiting both types of data. Our approach showed better performances with respect to other MRF-based methods as well as classical methods disregarding reliability information. Optimal (in the Maximum A-Posteriori probability sense) estimates of the 3D and confidence images are obtained by minimizing the energy functional by using simulated annealing.