Yes / An ability to predict the time-to-contact (TTC) of moving objects that become momentarily hidden is advantageous in everyday life and could be particularly so in fast-ball sports. Prediction motion (PM) experiments have sought to test this ability using tasks where a disappearing target moves towards a stationary destination. Here, we developed two novel versions of the PM task in which the destination either moved away from (Chase) or towards (Attract) the moving target. The target and destination moved with different speeds such that collision occurred 750, 1000 or 1250ms after target occlusion. To determine if domain-specific experience conveys an advantage in PM tasks, we compared the performance of different sporting groups ranging from internationally competing athletes to non-sporting controls. There was no difference in performance between sporting groups and non-sporting controls but there were significant and independent effects on response error by target speed, destination speed and occlusion period. We simulated these findings using a revised version of the linear TTC model of response timing for PM tasks (Yakimoff et al. 1987, 1993) in which retinal input from the moving destination biases the internal representation of the occluded target. This revision closely reproduced the observed patterns of response error and thus describes a means by which the brain might estimate TTC when the target and destination are in motion.

This thesis is concerned with the accuracy of our estimation of time to make contact with an approaching object as measured by the “Prediction Motion” (PM) technique. The PM task has commonly been used to measure the ability to judge time to contact (TTC). In a PM task, the observer's view of the target is occluded for some period leading up to the moment of impact. The length of the occlusion period is varied and the observer signals the moment of impact by pressing a response key. The interval separating the moment of occlusion and the response is interpreted as the observer's estimate of TTC made at the moment of occlusion. This technique commonly produces large variability and systematic underestimation. The possibility that this reflects genuine perceptual errors has been discounted by most writers, since this seems inconsistent with the accuracy of interceptive actions in real life. Instead, the poor performance in the PM task has been attributed to problems with the PM technique. Several hypotheses have been proposed to explain the poor PM performance. The motion extrapolation hypothesis asserts that some form of mental representation of the occluded part of the trajectory is used to time the PM response; the errors in PM performance are attributed to errors in reconstructing the target motion. The clocking hypothesis assumes that the TTC is accurately perceived at the moment of occlusion and that errors arise in delaying the response for the required period. The fear-of-collision hypothesis proposes that the underestimation seen in the PM tasks reflects a precautionary tendency to anticipate the estimated moment of contact. This thesis explores the causes of the errors in PM measurements. Experiments 1 and 2 assessed the PM performance using a range of motion scenarios involving various patterns of movement of the target, the observer, or both. The possible contribution of clocking errors to the PM performance was assessed by a novel procedure designed to measure errors in the wait-and-respond component of the PM procedure. In both experiments, this procedure yielded a pattern of systematic underestimation and high variability similar to that in the TTC estimation task. Experiment 1 found a small effect of motion scenario on TTC estimation. However, this was not evident in Experiment 2. The collision event simulated in Experiment 2 did not involve a solid collision. The target was simply a rectangular frame marked on a tunnel wall. At the moment of “contact”, the observers passed “through” the target without collision. However, there was still systematic underestimation of TTC and there was little difference between the estimates obtained in Experiments 1 and 2. Overall, the results of Experiments 1 and 2 were seen as inconsistent with either the motion extrapolation hypothesis or the fear-of-collision hypothesis. It was concluded that observers extracted an estimate of the TTC based on optic TTC information at a point prior to the moment of collision, and used a timing process to count down to the moment of response. The PM errors were attributed to failure in this timing process. The results of these experiments were seen as implying an accurate perception of TTC. It was considered possible that in Experiments 1 and 2 observers based their TTC judgements on either the retinal size or the expansion rate of the target rather than TTC. Experiments 3 and 4 therefore investigated estimation of TTC using a range of simulated target velocities and sizes. TTC estimates were unaffected by the resulting variation in expansion rate and size, indicating that TTC, rather than retinal size or image expansion rate per se, was used to time the observers' response. The accurate TTC estimation found in Experiments 1-4 indicates that the TTC processing is very robust across a range of stimulus conditions. Experiment 5 further explored this robustness by requiring estimation of TTC with an approaching target which rotated in the frontoparallel plane. It was shown that moderate but not fast rates of target rotation induced an overestimation of TTC. However, observers were able to discriminate between TTCs for all rates of rotation. This shows that the extraction of TTC information is sensitive to perturbation of the local motion of the target border, but it implies that, in spite of these perturbations, the mechanism is flexible enough to pick up the optic TTC information provided by the looming of the retinal motion envelop of the rotating stimulus.

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Many people with multiple sclerosis (MS) have difficulty with walking, which can decrease their sense of mobility. Gait stability was investigated by studying stride parameters and head movements at preferred and fixed speeds in those with MS. First, walking gait data were recorded at preferred and fixed walking speeds from 19 individuals with MS and 19 controls. Traditional gait parameters were compared, as was swing foot to center of mass (CoM) timing at mid-swing. Second, walking gait data in healthy young adults (n=20) were recorded at preferred speed and while stepping over an obstacle. Study 2 developed novel swing definitions, measures of coordination between the swing foot and body CoM, and head movements as they pertain to field of view orientation during walking. Third, these novel measures were used to study the swing phase of walking in people with MS. The first investigation revealed that the MS group walked with lengthened dual support times across all speeds, but shortened swing time and altered swing foot timing at fixed speeds in comparison to controls. Those with MS adopted a gait strategy with increased dual support time, despite forcing changes to swing that may reduce gait stability. In the second investigation, novel measures of swing showed alterations to phases of swing and in coordination between the swing foot and CoM under different gait tasks. This study also showed that the field of view was closer to the body during obstacle condition steps compared with unobstructed gait. In the third study, these novel measures showed that at all speeds the MS group shortened early swing and lengthened mid swing while late swing remained unchanged compared with controls. Coordination measures illustrated adaptations in swing foot dynamics that may partially ameliorate altered swing foot timing. The MS group oriented the field of view closer to the body earlier in swing compared with controls. Those with MS have functionally adapted swing to increase time over the stance foot and rely more on visual perception, yet shorter early swing may afford fewer opportunities to plan a step or cope with gait disturbances while walking.

This thesis investigates the use of binocular information for motion-in-depth (MID) perception. There are at least two different types of binocular information available to the visual system from which to derive a perception of MID: changing disparity (CD) and inter-ocular velocity differences (IOVD). In the following experiments, we manipulate the availability of CD and IOVD information in order to assess the relative influence of each on MID judgements. In the first experiment, we assessed the relative effectiveness of CD and IOVD information for MID detection, and whether the two types of binocular information are processed by separate mechanisms with differing characteristics. Our results suggest that, both CD and IOVD information can be utilised for MID detection, yet, the relative dependence on either of these types of MID information varies between observers. We then went on to explore the contribution of CD and IOVD information to time-to-contact (TTC) perception, whereby an observer judges the time at which an approaching stimulus will contact them. We confirmed that the addition of congruent binocular information to looming stimuli can influence TTC judgements, but that there is no influence from binocular information indicating no motion. Further to this, we found that observers could utilise both CD and IOVD for TTC judgements, although once again, individual receptiveness to CD and/or IOVD information varied. Thus, we demonstrate that the human visual system is able to process both CD and IOVD information, but the influence of either (or both) of these cues on an individual’s perception has been shown to be mutually independent.

In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.