River basin surveillance using remotely sensed data: a water resources information management system

This thesis describes the development of an operational river basin water resources information management system. The river or drainage basin is the fundamental unit of the system; in both the modelling and prediction of hydrological processes, and in the monitoring of the effect of catchment management policies. A primary concern of the study is the collection of sufficient and sufficiently accurate information to model hydrological processes. Remote sensing, in combination with conventional point source measurement, can be a valuable source of information, but is often overlooked by hydrologists, due to the cost of acquisition and processing. This thesis describes a number of cost effective methods of acquiring remotely sensed imagery, from airborne video survey to real time ingestion of meteorological satellite data. Inexpensive micro-computer systems and peripherals are used throughout to process and manipulate the data. Spatial information systems provide a means of integrating these data with topographic and thematic cartographic data, and historical records. For the system to have any real potential the data must be stored in a readily accessible format and be easily manipulated within the database. The design of efficient man-machine interfaces and the use of software enginering methodologies are therefore included in this thesis as a major part of the design of the system. The use of low cost technologies, from micro-computers to video cameras, enables the introduction of water resources information management systems into developing countries where the potential benefits are greatest.

Image usefulness of compressed surveillance footage with different scene contents

The police use both subjective (i.e. police staff) and automated (e.g. face recognition systems) methods for the completion of visual tasks (e.g person identification). Image quality for police tasks has been defined as the image usefulness, or image suitability of the visual material to satisfy a visual task. It is not necessarily affected by any artefact that may affect the visual image quality (i.e. decrease fidelity), as long as these artefacts do not affect the relevant useful information for the task. The capture of useful information will be affected by the unconstrained conditions commonly encountered by CCTV systems such as variations in illumination and high compression levels. The main aim of this thesis is to investigate aspects of image quality and video compression that may affect the completion of police visual tasks/applications with respect to CCTV imagery. This is accomplished by investigating 3 specific police areas/tasks utilising: 1) the human visual system (HVS) for a face recognition task, 2) automated face recognition systems, and 3) automated human detection systems. These systems (HVS and automated) were assessed with defined scene content properties, and video compression, i.e. H.264/MPEG-4 AVC. The performance of imaging systems/processes (e.g. subjective investigations, performance of compression algorithms) are affected by scene content properties. No other investigation has been identified that takes into consideration scene content properties to the same extend. Results have shown that the HVS is more sensitive to compression effects in comparison to the automated systems. In automated face recognition systems, `mixed lightness' scenes were the most affected and `low lightness' scenes were the least affected by compression. In contrast the HVS for the face recognition task, `low lightness' scenes were the most affected and `medium lightness' scenes the least affected. For the automated human detection systems, `close distance' and `run approach' are some of the most commonly affected scenes. Findings have the potential to broaden the methods used for testing imaging systems for security applications.

Deep Head Pose: Gaze-Direction Estimation in Multimodal Video

In this paper we present a convolutional neuralnetwork (CNN)-based model for human head pose estimation inlow-resolution multi-modal RGB-D data. We pose the problemas one of classification of human gazing direction. We furtherfine-tune a regressor based on the learned deep classifier. Next wecombine the two models (classification and regression) to estimateapproximate regression confidence. We present state-of-the-artresults in datasets that span the range of high-resolution humanrobot interaction (close up faces plus depth information) data tochallenging low resolution outdoor surveillance data. We buildupon our robust head-pose estimation and further introduce anew visual attention model to recover interaction with theenvironment. Using this probabilistic model, we show thatmany higher level scene understanding like human-human/sceneinteraction detection can be achieved. Our solution runs inreal-time on commercial hardware

An in-depth characterisation of neonatal seizures by early continuous video-EEG analysis

Introduction Seizures are harmful to the neonatal brain; this compels many clinicians and researchers to persevere further in optimizing every aspects of managing neonatal seizures. Aims To delineate the seizure profile between non-cooled versus cooled neonates with hypoxic-ischaemic encephalopathy (HIE), in neonates with stroke, the response of seizure burden to phenobarbitone and to quantify the degree of electroclinical dissociation (ECD) of seizures. Methods The multichannel video-EEG was used in this research study as the gold standard to detect seizures, allowing accurate quantification of seizure burden to be ascertained in term neonates. The entire EEG recording for each neonate was independently reviewed by at least 1 experienced neurophysiologist. Data were expressed in medians and interquartile ranges. Linear mixed models results were presented as mean (95% confidence interval); p values <0.05 were deemed as significant. Results Seizure burden in cooled neonates was lower than in non-cooled neonates [60(39-224) vs 203(141-406) minutes; p=0.027]. Seizure burden was reduced in cooled neonates with moderate HIE [49(26-89) vs 162(97-262) minutes; p=0.020] when compared with severe HIE. In neonates with stroke, the background pattern showed suppression over the infarcted side and seizures demonstrated a characteristic pattern. Compared with 10 mg/kg, phenobarbitone doses at 20 mg/kg reduced seizure burden (p=0.004). Seizure burden was reduced within 1 hour of phenobarbitone administration [mean (95% confidence interval): -14(-20 to -8) minutes/hour; p<0.001], but seizures returned to pre-treatment levels within 4 hours (p=0.064). The ECD index in cooled, non-cooled neonates with HIE, stroke and in neonates with other diagnoses were 88%, 94%, 64% and 75% respectively. Conclusions Further research exploring the treatment effects on seizure burden in the neonatal brain is required. A change to our current treatment strategy is warranted as we continue to strive for more effective seizure control, anchored with use of the multichannel EEG as the surveillance tool.