ASSESSMENT OF CHROMATIC ABERRATIONS FOR GOPRO 3 CAMERAS IN UNDERWATER ENVIRONMENTS

With underwater photogrammetric mapping becoming more prominent due to the lower costs for waterproof cameras as well as lower costs for underwater platforms, the aim of this research is to investigate chromatic aberration in underwater environments. Chromatic aberration in in-air applications is to be known to systematically influence the observations of up to a few pixels. In order to achieve pixel-level positioning accuracy, this systematic influence needs further investigation. However, while chromatic aberration studies have been performed for in-air environments, there is a lack of research to quantify the influence of chromatic aberration in underwater environments. Using images captured in a water tank from three different GoPro cameras in five datasets, we investigate possible chromatic aberration by running two different adjustments on the extracted red (R), green (G) and blue (B) bands. The first adjustment is an adjustment that calculates the interior orientation parameters for each set of images independently in a free network adjustment. The second adjustment solves for all interior orientation parameters (for R, G, and B channels) in a combined adjustment per camera, constraining the point observations in object space. We were able to quantify significant chromatic aberrations in our evaluation, with the largest aberrations observed for red band followed by green and blue

Perceptual Evaluation of Mitigation Approaches of Impairments due to Spatial Undersampling in Binaural Rendering of Spherical Microphone Array Data

Spherical microphone arrays (SMAs) are widely used to capture spatial sound fields that can then be rendered in various ways as a virtual acoustic environment (VAE) including headphone-based binaural synthesis. Several practical limitations have a significant impact on the fidelity of the rendered VAE. The finite number of microphones of SMAs leads to spatial undersampling of the captured sound field, which, on the one hand, induces spatial aliasing artifacts and, on the other hand, limits the order of the spherical harmonics (SH) representation. Several approaches have been presented in the literature that aim to mitigate the perceptual impairments due to these limitations. In this article, we present a listening experiment evaluating the perceptual improvements of binaural rendering of undersampled SMA data that can be achieved using state-of-the-art mitigation approaches. In particular, we examined the Magnitude Least-Squares algorithm, the Bandwidth Extraction Algorithm for Microphone Arrays, Spherical Head Filters, SH Tapering, and a newly proposed equalization filter. In the experiment, subjects rated the perceived differences between a dummy head and the corresponding SMA auralization. We found that most mitigation approaches lead to significant perceptual improvements, even though audible differences to the reference remain

Relative periodic orbits in point vortex systems

We give a method to determine relative periodic orbits in point vortex systems: it consists mainly into perform a symplectic reduction on a fixed point submanifold in order to obtain a two-dimensional reduced phase space. The method is applied to point vortices systems on a sphere and on the plane, but works for other surfaces with isotropy (cylinder, ellipsoid, ...). The method permits also to determine some relative equilibria and heteroclinic cycles connecting these relative equilibria.Comment: 27 pages, 17 figure

INTRODUCING A FRAMEWORK FOR CONFLATING ROAD NETWORK DATA WITH SEMANTIC WEB TECHNOLOGIES

Road network asset management is a challenging task as many data sources with different road asset location accuracies are available. In Australia and New Zealand transport agencies are investigating into harmonisation of road asset data, whereby two or more data sets are merged to create a new data set. Currently, identifying relations between road assets of the same meaning is not always possible, as road authorities of these countries use their own data structures and standards. This paper employs SemanticWeb Technologies, such as RDF/Turtle ontologies and semantic rules to enable road network conflation (merge multiple data sets without creating a new data set) as a first step towards data harmonisation by means of information exchange, and shifts road network data from intersections and road nodes to data sets considering the accuracy of the data sets in the selected area. The data integration from GeoJSON into RDF/Turtle files is processed with Python. A geographic coordinates shifting algorithm reads unique data entries that have been extracted from RDF/Turtle into JSON-LD and saves the processed data in their origin file format, so that a closed data flow can be approached

Creation of 3D models from large unstructured image and video datasets

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

Non-parametric belief propagation for mobile mapping sensor fusion

© 2016 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group. 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

PARAMETER OPTIMIZATION FOR A THERMAL SIMULATION OF AN URBAN AREA

Urban heat island (UHI) phenomenon is a significant challenge in urban planning, and accurate temperature predictions are crucial for effective decision-making. The choice of material parameters is crucial to simulate a realistic temperature distribution and identify potential UHIs. This paper introduces a framework for optimizing the material properties based on sensors boxes placed upon surfaces made up by different materials. The methodology covers an optimization approach for the material properties to achieve accurate surface temperature simulation. The results, which involved close-range validation and macro-scale validation, show a significant improvement in the agreement between the simulated and measured temperature time series, especially for tiled roofs and asphalt roads. However, the accuracy for grassland areas decreased, possibly due to differences in soil moisture. Overall, the proposed framework shows promising results for future work in improving the accuracy of thermal simulation of urban areas

Towards a General Theory of Neural Computation Based on Prediction by Single Neurons

Although there has been tremendous progress in understanding the mechanics of the nervous system, there has not been a general theory of its computational function. Here I present a theory that relates the established biophysical properties of single generic neurons to principles of Bayesian probability theory, reinforcement learning and efficient coding. I suggest that this theory addresses the general computational problem facing the nervous system. Each neuron is proposed to mirror the function of the whole system in learning to predict aspects of the world related to future reward. According to the model, a typical neuron receives current information about the state of the world from a subset of its excitatory synaptic inputs, and prior information from its other inputs. Prior information would be contributed by synaptic inputs representing distinct regions of space, and by different types of non-synaptic, voltage-regulated channels representing distinct periods of the past. The neuron's membrane voltage is proposed to signal the difference between current and prior information (“prediction error” or “surprise”). A neuron would apply a Hebbian plasticity rule to select those excitatory inputs that are the most closely correlated with reward but are the least predictable, since unpredictable inputs provide the neuron with the most “new” information about future reward. To minimize the error in its predictions and to respond only when excitation is “new and surprising,” the neuron selects amongst its prior information sources through an anti-Hebbian rule. The unique inputs of a mature neuron would therefore result from learning about spatial and temporal patterns in its local environment, and by extension, the external world. Thus the theory describes how the structure of the mature nervous system could reflect the structure of the external world, and how the complexity and intelligence of the system might develop from a population of undifferentiated neurons, each implementing similar learning algorithms