Underwater environments provide significant opportunities for innovative research in various scientific fields and can have high economic interest in numerous commercial applications. To monitor diverse phenomena in an unattended manner, it is necessary to develop suitable observatory systems. In this work we describe the development of an autonomous submarine imaging system for bubble detection, the implementation of which addresses the challenges of deployment in extreme environments, such as those encountered in highly active underwater hydrothermal fields. To address the bubble detection problem, we train state-of-the-art object detection models using a manually collected dataset containing a large number of images in a real environment. Our models exhibit promising results both in terms of evaluation metrics and qualitative assessment.

We propose a training-free demosaicing method for multi-spectral filter array sensors by exploiting the concept of deep image prior. The proposed method does not require hard-to-obtain ground truth high resolution data corresponding to the bands of the filter array, and can thus be easily employed to arbitrary multispectral filter array configurations. Besides its training-free nature, the proposed method achieves improved performance with respect to state-of-the-art learning-based multispectral demosaicing methods.

We explore the use of production Machine Learning (ML) frameworks for automatically building ML models for cloud-based services that exploit geospatial big data and value-added products. We combine two widely used production ML frameworks to hierarchically decompose the tasks involved with the fetching and preprocessing of the data as well as with model training, evaluation, and selection. We assess the usability, reproducibility and performance of the frameworks both qualitatively and quantitatively. We examine the challenging case of a cloud-based seabed mapping service that process multispecrtal multibeam echosounder data captured in different marine surveys, involving a number of data processing and machine learning tasks.

Robots assisting humans with some specific tasks have been demonstrated on several occasions. A further challenging idea is to anticipate human needs by mining the future demand from the next action prediction. To trigger this anticipation mechanism a robot has to recognize what the human is doing now, foresee what the human will do next, and from their connection guesstimating what to do to help. We propose here a deep network combining the essential components of this challenging process leading to foreseeing the help that can be provided in human-robot collaboration.

A methodology for 3D surface modeling from a single image is proposed. The principal novelty is concave and specular surface modeling without any externally imposed prior. The main idea of the method is to use BRDFs and generated rendered surfaces, to transfer the normal field, computed for the generated samples, to the unknown surface. The transferred information is adequate to blow and sculpt the segmented image mask in to a bas-relief of the object. The object surface is further refined basing on a photo-consistency formulation that relates for error minimization the original image and the modeled object.

We propose a novel approach to human action recognition, with motion capture data (MoCap), based on grouping sub-body parts. By representing configurations of actions as manifolds, joint positions are mapped on a subspace via principal geodesic analysis. The reduced space is still highly informative and allows for classification based on a non-parametric Bayesian approach, generating behaviors for each sub-body part. Having partitioned the set of joints, poses relative to a sub-body part are exchangeable, given a specified prior and can elicit, in principle, infinite behaviors. The generation of these behaviors is specified by a Dirichlet process mixture. We show with several experiments that the recognition gives very promising results, outperforming methods requiring temporal alignment.

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time.