A fast webcam photogrammetric system to support optical imaging of brain activity

Optical topography (OT) is an emerging neuroimaging technique utilising the tight coupling between neural activity and regional cerebral blood flow to monitor relative regional changes of haemoglobin concentration. OT systems are compact, low cost, easily portable, and relatively tolerant of body movements enabling clinical diagnosis, psychological experiments and even monitoring brain activities during daily living. There is a requirement for such systems to present their output functional data in a brain model based coordinate space in order to map to the signal source with brain anatomy. However OT data are obtained from a network of OT sensing devices (optodes) placed in contact with the head surface and cannot capture structural information of the underlying brain which might otherwise be used for registration. An appropriate registration method, widely used in Electroencephalography (EEG), is the 10-20 system which utilises bony landmarks as common points to co-register locations on the scalp with a brain model to a repeatability of a few millimetres in clinical applications to an MRI set of reference points. Inheriting the low cost and portability of OT, this thesis develops and validates a novel registration approach utilising off-the-shelf webcam technology in combination with photogrammetric bundle adjustment techniques in order to reliably coordinate targets on optodes and bony landmarks within the 10:20 reference frame to an accuracy of better than 1mm. Initial research includes an assessment of the 3D coordination accuracy, precision and stability of a series of low cost webcams in order to prove their suitability for clinical applications. Results demonstrate the capability of a system based on these cameras to reliably coordinate 3D target locations to the order of 0.5mm and better. Difficulties in automated clinical target image extraction due to poor image quality are circumvented through the development of new target image detection methods. Incremental improvements in image quality from successive webcam generations, up to and including the latest HD systems, are shown to increase coordination accuracy by one order of magnitude. The result is a novel webcam photogrammetric system that is able to rapidly and consistently coordinate targets on optodes and bony landmarks to better than 1mm in OT studies and is able to take advantage of the rapid advances being made in consumer webcam technology. The system is proven in pre-clinical studies to evaluate its coordination accuracy and in simulated clinical OT studies with a head-sized phantom conducted in collaboration with Department of Medical Physics and Bioengineering. Clinical OT studies with human subjects, demonstrate the capability of the system to continuously coordinate targets on optodes and scalp and detect differential movement between optodes and scalp which would invalidate a static registration procedure

Time perception in infants : an exploration using eye tracking methodology

Using eye tracking methodology, this thesis investigates if four-month-old infants can perceive short time intervals, as sensitivity to temporal parameters underlies cognitive development. Whilst the thesis draws on theoretical frameworks of understanding how animals and humans perceive short time intervals, it extends the framework, enabling the application of one developmental model of timing to be used with much younger children (four-months-old infants, formerly children aged three years old). Six eye tracking experiments investigated how infants perceive time intervals and the factors that might influence that ability. One hundred and nine typically developing four-month- old infants participated in the experiments, from which six main findings emerged. First, overt behavioural evidence of infants keeping time over several stimulus sequences was obtained by using eye tracking methodology. This is the first time that this ability has been demonstrated using eye tracking which clearly indicates the focus of the infant's attention. Second, using naturalistic stimulus sequences young infants demonstrated a clear ability to perceive a number of different time intervals within one testing session, indicating the importance of using salient stimuli. Third, various influencing factors were observed to facilitate or hinder time perception such as speed of sequence presentation and the simultaneous presentation of both auditory and visual stimulus respectively. Fourth, the use of different information processing strategies to encode the stimuli revealed further differences in time perception. Fifth, and for the first time in these types of experiments, an infant-adapted temporal generalisation task has revealed similar results to children and animals. Sixth, infants demonstrated continued gaze-following over several stimulus sequences after a period of mutual gaze. Several issues concerning the processes underlying infant cognitive development are discussed together with their implications for later learning

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 141)

This special bibliography lists 267 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1975

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

REAL-TIME CAPTURE AND RENDERING OF PHYSICAL SCENE WITH AN EFFICIENTLY CALIBRATED RGB-D CAMERA NETWORK

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. With the recent explosive growth of Augmented Reality (AR) and Virtual Reality (VR) platforms, utilizing camera RGB-D camera networks to capture and render dynamic physical space can enhance immersive experiences for users. To maximize coverage and minimize costs, practical applications often use a small number of RGB-D cameras and sparsely place them around the environment for data capturing. While sparse color camera networks have been studied for decades, the problems of extrinsic calibration of and rendering with sparse RGB-D camera networks are less well understood. Extrinsic calibration is difficult because of inappropriate RGB-D camera models and lack of shared scene features. Due to the significant camera noise and sparse coverage of the scene, the quality of rendering 3D point clouds is much lower compared with synthetic models. Adding virtual objects whose rendering depend on the physical environment such as those with reflective surfaces further complicate the rendering pipeline. In this dissertation, I propose novel solutions to tackle these challenges faced by RGB-D camera systems. First, I propose a novel extrinsic calibration algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Second, I propose a novel rendering pipeline that can capture and render, in real-time, dynamic scenes in the presence of arbitrary-shaped reflective virtual objects. Third, I have demonstrated a teleportation application that uses the proposed system to merge two geographically separated 3D captured scenes into the same reconstructed environment. To provide a fast and robust calibration for a sparse RGB-D camera network, first, the correspondences between different camera views are established by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic using rigid transformation that is optimal only for pinhole cameras, different view transformation functions including rigid transformation, polynomial transformation, and manifold regression are systematically tested to determine the most robust mapping that generalizes well to unseen data. Third, the celebrated bundle adjustment procedure is reformulated to minimize the global 3D projection error so as to fine-tune the initial estimates. To achieve a realistic mirror rendering, a robust eye detector is used to identify the viewer\u27s 3D location and render the reflective scene accordingly. The limited field of view obtained from a single camera is overcome by our calibrated RGB-D camera network system that is scalable to capture an arbitrarily large environment. The rendering is accomplished by raytracing light rays from the viewpoint to the scene reflected by the virtual curved surface. To the best of our knowledge, the proposed system is the first to render reflective dynamic scenes from real 3D data in large environments. Our scalable client-server architecture is computationally efficient - the calibration of a camera network system, including data capture, can be done in minutes using only commodity PCs

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Development of a passive MEG stimulus for measurement of the binaural masking level difference

The ability to hear a target signal over background noise is an important aspect of efficient hearing in everyday situations. This mechanism depends on binaural hearing whenever there are differences in the inter-aural timing of inputs from the noise and the signal. Impairments in binaural hearing may underlie some auditory processing disorders, for example temporal-lobe epilepsies. The binaural masking level difference (BMLD) measures the advantage in detecting a tone whose inter-aural phase differs from that of the masking noise. BMLD’s are typically estimated psychophysically, but this is challenging in children or those with cognitive impairments. The aim of this doctorate is to design a passive measure of BMLD using magnetoencephalography (MEG) and test this in adults, children and patients with different types of epilepsy. The stimulus consists of Gaussian background noise with 500-Hz tones presented binaurally either in-phase or 180° out-of-phase between the ears. Source modelling provides the N1m amplitude for the in-phase and out-of-phase tones, representing the extent of signal perception over background noise. The passive BMLD stimulus is successfully used as a measure of binaural hearing capabilities in participants who would otherwise be unable to undertake a psychophysical task

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved