Stone anchors comprise a significant portion of observable underwater cultural heritage in the Mediterranean and provide evidence for trade networks as early as the Bronze Age. Full documentation of these anchors, however, often requires their removal from their underwater environment, especially to calculate mass. We offer a methodology for using photogrammetry to record stone anchors still in situ and calculate their approximate mass. We compare measurements derived using measuring tapes with those derived using two different software programs for photogrammetric analysis, PhotoModeler Scanner (Eos Systems, Inc.) and PhotoScan Pro (Agisoft). First, we analyze stone anchors that had previously been removed from the underwater environment to establish a reference methodology. Next, we implement this methodology in an underwater survey off the southern coastline of Cyprus. Linear measurements for both programs correlate closely with those attained via measuring tape. The resulting estimates of volume of anchors in situ and on land are slightly greater using the photogrammetric methodology than the reference volumes obtained using a water displacement methodology. Overall, as an analytical tool, this methodology generates detailed surface information in minimal time underwater and preserves data for future analysis without necessitating the removal of the anchor from its underwater environment.
ABSTRACT:Exploration of various places using low-cost camera solutions over decades without having a photogrammetric application in mind has resulted in large collections of images and videos that may have significant cultural value. The purpose of collecting this data is often to provide a log of events and therefore the data is often unstructured and of varying quality. Depending on the equipment used there may be approximate location data available for the images but the accuracy of this data may also be of varying quality. In this paper we present an approach that can deal with these conditions and process datasets of this type to produce 3D models. Results from processing the dataset collected during the discovery and subsequent exploration of the HMAS Sydney and HSK Kormoran wreck sites shows the potential of our approach. The results are promising and show that there is potential to retrieve significantly more information from many of these datasets than previously thought possible.
Many different forms of sensor fusion have been proposed each with its own niche. We propose a method of fusing multiple different sensor types. Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional (3D) point clouds. We propose using a non-parametric belief propagation similar to Sudderth et al's work to fuse different sensors. This technique allows continuous variables to be used, is trivially parallel making it suitable for modern many-core processors, and easily accommodates varying types and combinations of sensors. By defining the relationships between common sensors, a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors. This allows the use of unreliable sensors which firstly, may start and stop providing data at any time and secondly, the integration of new sensor types simply by defining their relationship with existing sensors. These features allow a flexible framework to be developed which is suitable for many tasks. Using an abstract algorithm, we can instead focus on the relationships between sensors. Where possible we use the existing relationships between sensors rather than developing new ones. These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network. In this paper, we present the initial results from this technique and the intended course for future work.
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