This paper presents a vision-based underwater mapping system, which is demonstrated in an archaeological survey of the submerged ancient town of Pavlopetri. The snorkeler or diver operated system provides a low cost alternative to the use of an AUV or ROV in shallow waters. The system produces textured three-dimensional models, which contain significantly more information than traditional archaeological survey methods. The photo-realistic maps that are produced allow further archaeological research to be performed, without diving on a site during the restrictive time limitations of permits and field seasons. The hardware and software components of the mapping system and its method of operation are described, and initial results are presented and discussed.

This paper demonstrates an implementation of pool-based active learning through uncertainty sampling using a Variational Dirichlet Process (VDP) model. The VDP is used for both pre-clustering and classification, and is extended to incorporate fixed labels from an oracle (human annotator). Three different uncertainty sampling techniques are explored - least confident sampling, margin sampling and entropy based sampling. Clustering with the VDP is done in a completely unsupervised manner, without the need to specify the number of clusters. This appears particularly useful in improving the results when there are only few labelled samples, or if the cost of labelling is high. Results are shown for a toy dataset and the performance is compared to similar implementations using an Expectation Maximisation model (EM) and a Naive Bayes classifier (NB). The VDP active learning framework is tested on a stereo image dataset obtained by an autonomous underwater vehicle that covers several linear kilometres and consists of thousands of stereo image pairs. Our results show that combining an active learning strategy with the VDP significantly reduces the number of labelled images required to achieve a desired level of accuracy.