The automatic or semi-automatic research of archaeological findings includes some methodologies and algorithms of the Computer Vision. Reconstruction of a scene is one of the key step to get the solution to that challenge. This paper will address a methodology to reconstruction underwater scenes with mosaicing techniques. The reconstruction of scene will be the video-mosaic of sea bottom landscapes starting from single video frames. The methodology is based on the evaluation of the optic °ow in between frames, and its motion estimation has been evaluated on the extracted features from the common areas of consecutive pairs frames. This approach carried out the motion model from a geometric projection framework. The camera movement estimation is a second key point in the mosaicing problem. The used methodology have to be enough robust to achieve a good performance because of the high level of noise and turbulence involved in sea bottom video acquisition. For this purpose geometrical transformations have been used to map each frame global into a unique big common reference frame which dimensions are similar to the union of frames.
IALUNA, R., GAGLIANO, G., GRAVILI, D., TEGOLO D. (2006). Tecsis: Low-Cost Methodology To Distinguish Archaeological Findings. CHEMISTRY IN ECOLOGY, 22(8), 403-410 [10.1080/02757540600738500].
Tecsis: Low-Cost Methodology To Distinguish Archaeological Findings
TEGOLO, Domenico
2006-01-01
Abstract
The automatic or semi-automatic research of archaeological findings includes some methodologies and algorithms of the Computer Vision. Reconstruction of a scene is one of the key step to get the solution to that challenge. This paper will address a methodology to reconstruction underwater scenes with mosaicing techniques. The reconstruction of scene will be the video-mosaic of sea bottom landscapes starting from single video frames. The methodology is based on the evaluation of the optic °ow in between frames, and its motion estimation has been evaluated on the extracted features from the common areas of consecutive pairs frames. This approach carried out the motion model from a geometric projection framework. The camera movement estimation is a second key point in the mosaicing problem. The used methodology have to be enough robust to achieve a good performance because of the high level of noise and turbulence involved in sea bottom video acquisition. For this purpose geometrical transformations have been used to map each frame global into a unique big common reference frame which dimensions are similar to the union of frames.File | Dimensione | Formato | |
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