IEEE Transactions on Pattern Analysis and Machine Intelligence
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integration of visual modules: an extension of the Marr paradigm
Integration of visual modules: an extension of the Marr paradigm
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Physical modeling and combination of range and intensity edge data
CVGIP: Image Understanding
Geometric computation for machine vision
Geometric computation for machine vision
Camera calibration without feature extraction
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Machine Vision and Applications - Special issue on performance evaluation
Computing and simplifying 2D and 3D continuous skeletons
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
Integrating Vision Modules: Stereo, Shading, Grouping, and Line Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, the problem of underwater scene understanding from multisensory data is addressed. Acoustic and optical devices on board an underwater vehicle are used to sense the environment in order to produce an output which is readily understandable even by an inexperienced operator. The main idea is to integrate multiple sensory data by geometrically registering data to a model. In this way, vehicle pose is derived, and the model objects can be superimposed on actual images, generating an augmented reality representation. Results on a real underwater scene are provided, showing the effectiveness of the proposed approach.