Designing a Computer Vision System for Underwater Robotic Interventions

This thesis deals with the challenging problem of designing systems able to perceive objects in underwater environments. In the last few decades research activities in robotics have advanced the state of art regarding intervention capabilities of autonomous systems. State of art in fields such as localization and navigation, real time perception and cognition, safe action and manipulation capabilities, applied to ground environments (both indoor and outdoor) has now reached such a readiness level that it allows high level autonomous operations. On the opposite side, the underwater environment remains a very difficult one for autonomous robots. Water influences the mechanical and electrical design of systems, interferes with sensors by limiting their capabilities, heavily impacts on data transmissions, and generally requires systems with low power consumption in order to enable reasonable mission duration. Interest in underwater applications is driven by needs of exploring and intervening in environments in which human capabilities are very limited. Nowadays, most underwater field operations are carried out by manned or remotely operated vehicles, deployed for explorations and limited intervention missions. Manned vehicles, directly on-board controlled, expose human operators to risks related to the stay in field of the mission, within a hostile environment. Remotely Operated Vehicles (ROV) currently represent the most advanced technology for underwater intervention services available on the market. These vehicles can be remotely operated for long time but they need support from an oceanographic vessel with multiple teams of highly specialized pilots. Vehicles equipped with multiple state-of-art sensors and capable to autonomously plan missions have been deployed in the last ten years and exploited as observers for underwater fauna, seabed, ship wrecks, and so on. On the other hand, underwater operations like object recovery and equipment maintenance are still challenging tasks to be conducted without human supervision since they require object perception and localization with much higher accuracy and robustness, to a degree seldom available in Autonomous Underwater Vehicles (AUV). This thesis reports the study, from design to deployment and evaluation, of a general purpose and configurable platform dedicated to stereo-vision perception in underwater environments. Several aspects related to the peculiar environment characteristics have been taken into account during all stages of system design and evaluation: depth of operation and light conditions, together with water turbidity and external weather, heavily impact on perception capabilities. The vision platform proposed in this work is a modular system comprising off-the-shelf components for both the imaging sensors and the computational unit, linked by a high performance ethernet network bus. The adopted design philosophy aims at achieving high flexibility in terms of feasible perception applications, that should not be as limited as in case of a special-purpose and dedicated hardware. Flexibility is required by the variability of underwater environments, with water conditions ranging from clear to turbid, light backscattering varying with daylight and depth, strong color distortion, and other environmental factors. Furthermore, the proposed modular design ensures an easier maintenance and update of the system over time. Performance of the proposed system, in terms of perception capabilities, has been evaluated in several underwater contexts taking advantage of the opportunity offered by the MARIS national project. Design issues like energy power consumption, heat dissipation and network capabilities have been evaluated in different scenarios. Finally, real-world experiments, conducted in multiple and variable underwater contexts, including open sea waters, have led to the collection of several datasets that have been publicly released to the scientific community. The vision system has been integrated in a state of the art AUV equipped with a robotic arm and gripper, and has been exploited in the robot control loop to successfully perform underwater grasping operations.

Co-registration of magnetoencephalography with magnetic resonance imaging using bite-bar-based fiducials and surface-matching

Objective: To introduce a new technique for co-registration of Magnetoencephalography (MEG) with magnetic resonance imaging (MRI). We compare the accuracy of a new bite-bar with fixed fiducials to a previous technique whereby fiducial coils were attached proximal to landmarks on the skull. Methods: A bite-bar with fixed fiducial coils is used to determine the position of the head in the MEG co-ordinate system. Co-registration is performed by a surface-matching technique. The advantage of fixing the coils is that the co-ordinate system is not based upon arbitrary and operator dependent fiducial points that are attached to landmarks (e.g. nasion and the preauricular points), but rather on those that are permanently fixed in relation to the skull. Results: As a consequence of minimizing coil movement during digitization, errors in localization of the coils are significantly reduced, as shown by a randomization test. Displacement of the bite-bar caused by removal and repositioning between MEG recordings is minimal (∼0.5 mm), and dipole localization accuracy of a somatosensory mapping paradigm shows a repeatability of ∼5 mm. The overall accuracy of the new procedure is greatly improved compared to the previous technique. Conclusions: The test-retest reliability and accuracy of target localization with the new design is superior to techniques that incorporate anatomical-based fiducial points or coils placed on the circumference of the head. © 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Use of fundus imaging in quantification of age-related macular change

This review will discuss the use of manual grading scales, digital photography, and automated image analysis in the quantification of fundus changes caused by age-related macular disease. Digital imaging permits processing of images for enhancement, comparison, and feature quantification, and these techniques have been investigated for automated drusen analysis. The accuracy of automated analysis systems has been enhanced by the incorporation of interactive elements, such that the user is able to adjust the sensitivity of the system, or manually add and remove pixels. These methods capitalize on both computer and human image feature recognition and the advantage of computer-based methodologies for quantification. The histogram-based adaptive local thresholding system is able to extract useful information from the image without being affected by the presence of other structures. More recent developments involve compensation for fundus background reflectance, which has most recently been combined with the Otsu method of global thresholding. This method is reported to provide results comparable with manual stereo viewing. Developments in this area are likely to encourage wider use of automated techniques. This will make the grading of photographs easier and cheaper for clinicians and researchers. © 2007 Elsevier Inc. All rights reserved.

The lateral and ventromedial prefrontal cortex work as a dynamic integrated system:evidence from FMRI connectivity analysis

Recent functional magnetic resonance imaging (fMRI) investigations of the interaction between cognition and reward processing have found that the lateral prefrontal cortex (PFC) areas are preferentially activated to both increasing cognitive demand and reward level. Conversely, ventromedial PFC (VMPFC) areas show decreased activation to the same conditions, indicating a possible reciprocal relationship between cognitive and emotional processing regions. We report an fMRI study of a rewarded working memory task, in which we further explore how the relationship between reward and cognitive processing is mediated. We not only assess the integrity of reciprocal neural connections between the lateral PFC and VMPFC brain regions in different experimental contexts but also test whether additional cortical and subcortical regions influence this relationship. Psychophysiological interaction analyses were used as a measure of functional connectivity in order to characterize the influence of both cognitive and motivational variables on connectivity between the lateral PFC and the VMPFC. Psychophysiological interactions revealed negative functional connectivity between the lateral PFC and the VMPFC in the context of high memory load, and high memory load in tandem with a highly motivating context, but not in the context of reward alone. Physiophysiological interactions further indicated that the dorsal anterior cingulate and the caudate nucleus modulate this pathway. These findings provide evidence for a dynamic interplay between lateral PFC and VMPFC regions and are consistent with an emotional gating role for the VMPFC during cognitively demanding tasks. Our findings also support neuropsychological theories of mood disorders, which have long emphasized a dysfunctional relationship between emotion/motivational and cognitive processes in depression.