Region-based approximation of probability distributions (for visibility between imprecise points among obstacles)

Let p and q be two imprecise points, given as probability density functions on R 2, and let R be a set of n line segments in R 2 . We study the problem of approximating the probability that p and q can see each other; that is, that the segment connecting p and q does not cross any segment of R. To solve this problem, we approximate each density function by a weighted set of polygons; a novel approach to dealing with probability density functions in computational geometry

Fuzzy optimisation based symbolic grounding for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophySymbolic grounding is a bridge between task level planning and actual robot sensing and actuation. Uncertainties raised by unstructured environments make a bottleneck for integrating traditional artificial intelligence with service robotics. In this research, a fuzzy optimisation based symbolic grounding approach is presented. This approach can handle uncertainties and helps service robots to determine the most comfortable base region for grasping objects in a fetch and carry task. Novel techniques are applied to establish fuzzy objective function, to model fuzzy constraints and to perform fuzzy optimisation. The approach does not have the short comings of others’ work and the computation time is dramatically reduced in compare with other methods. The advantages of the proposed fuzzy optimisation based approach are evidenced by experiments that were undertaken in Care-O-bot 3 (COB 3) and Robot Operating System (ROS) platforms

GEO-REFERENCED VIDEO RETRIEVAL: TEXT ANNOTATION AND SIMILARITY SEARCH

Ph.DDOCTOR OF PHILOSOPH

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Resource selection and abundance estimation of moose: Implications for caribou recovery in a human altered landscape

Woodland caribou (Rangifer tarandus caribou) are threatened across Canada due to human disturbance altering predator-prey dynamics. The niche specialization of caribou enables them to survive in nutrient-poor habitats spatially separated from other ungulates and their shared predators. The conversion of old-growth forests to young seral stands is hypothesized to increase the abundance of moose (Alces alces), the dominant prey for wolves (Canis lupus), resulting in apparent competition. We first examined habitat selection of moose in 2 regions with differing intensities of human disturbance in west-central Alberta and east-central British Columbia to assess how human disturbance affects the spatial separation of moose and caribou. We built resource selection functions with data from global positioning system (GPS) collars deployed on 17 moose (8 in a region with high and 9 in a region with low human disturbance) at 2 spatial scales. Our results indicated that moose in our study area make forage-risk tradeoffs in a hierarchical fashion similar to caribou, potentially eroding spatial separation in human disturbed landscapes. We also evaluated the spatial partitioning of resources by comparing resource use with GPS locations from 17 moose and 17 paired caribou using logistic regression. As expected, human disturbance decreased the resource partitioning between moose and caribou. Thus, systematic moose management and monitoring will be essential for caribou conservation. Currently, a Stratified Random Block (SRB) survey design is widely used to estimate moose populations, but these surveys are expensive and often result in imprecise population estimates when not corrected for sightability bias. We evaluated the application of distance sampling as an alternative to SRB surveys, especially for use in caribou ranges. To correct for moose missed on the transect line, where a detection rate of 100% is critical, we developed a sightability model using 21 radio-collared moose. After correcting for sightability, distance sampling was more precise and efficient than SRB surveys. In this way, more efficient distance sampling methodology can be an important tool for caribou conservation. Combined, our results showed the importance of moose management in caribou ranges due to decreased spatial separation between both ungulate species in disturbed landscapes

Application of Geographic Information Systems

The importance of Geographic Information Systems (GIS) can hardly be overemphasized in today’s academic and professional arena. More professionals and academics have been using GIS than ever – urban & regional planners, civil engineers, geographers, spatial economists, sociologists, environmental scientists, criminal justice professionals, political scientists, and alike. As such, it is extremely important to understand the theories and applications of GIS in our teaching, professional work, and research. “The Application of Geographic Information Systems” presents research findings that explain GIS’s applications in different subfields of social sciences. With several case studies conducted in different parts of the world, the book blends together the theories of GIS and their practical implementations in different conditions. It deals with GIS’s application in the broad spectrum of geospatial analysis and modeling, water resources analysis, land use analysis, infrastructure network analysis like transportation and water distribution network, and such. The book is expected to be a useful source of knowledge to the users of GIS who envision its applications in their teaching and research. This easy-to-understand book is surely not the end in itself but a little contribution to toward our understanding of the rich and wonderful subject of GIS

Recognition of Traffic Situations based on Conceptual Graphs

This work investigates the suitability of conceptual graphs for situation recognition. The scene graph is created in the form of a conceptual graph according to the concept type hierarchy, relation type hierarchy, rules and constraints using the previously obtained information about objects and lanes. The graphs are then matched using projection with the query conceptual graph, which represents the situation. The functionality of the model is shown on the real traffic situations

Patient-specific simulation for autonomous surgery

An Autonomous Robotic Surgical System (ARSS) has to interact with the complex anatomical environment, which is deforming and whose properties are often uncertain. Within this context, an ARSS can benefit from the availability of patient-specific simulation of the anatomy. For example, simulation can provide a safe and controlled environment for the design, test and validation of the autonomous capabilities. Moreover, it can be used to generate large amounts of patient-specific data that can be exploited to learn models and/or tasks. The aim of this Thesis is to investigate the different ways in which simulation can support an ARSS and to propose solutions to favor its employability in robotic surgery. We first address all the phases needed to create such a simulation, from model choice in the pre-operative phase based on the available knowledge to its intra-operative update to compensate for inaccurate parametrization. We propose to rely on deep neural networks trained with synthetic data both to generate a patient-specific model and to design a strategy to update model parametrization starting directly from intra-operative sensor data. Afterwards, we test how simulation can assist the ARSS, both for task learning and during task execution. We show that simulation can be used to efficiently train approaches that require multiple interactions with the environment, compensating for the riskiness to acquire data from real surgical robotic systems. Finally, we propose a modular framework for autonomous surgery that includes deliberative functions to handle real anatomical environments with uncertain parameters. The integration of a personalized simulation proves fundamental both for optimal task planning and to enhance and monitor real execution. The contributions presented in this Thesis have the potential to introduce significant step changes in the development and actual performance of autonomous robotic surgical systems, making them closer to applicability to real clinical conditions

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform