Posterior Cortical Atrophy (PCA) is a rare clinical syndrome characterised by the predominance of higher-order visual disturbances. Deficits result from a progressive neurodegeneration of occipito-temporal and occipito-parietal cortices. Due to its relative scarcity, many common symptoms of PCA, such as visuomotor dysfunction, have yet to be fully investigated. The current study sought to explore the visuomotor abilities of four individuals with PCA by testing their ability to reach out and grasp real objects under various viewing conditions. The patients demonstrated many of the same deficits as those seen in individuals with optic ataxia, including impaired grip scaling to peripheral targets, poor selection of stable grasp sites, and evidence of ‘magnetic misreaching’ – a pathological reaching bias towards the point of visual fixation. Unlike individuals with pure optic ataxia, however, the patients in the current study showed symptoms indicative of damage to the ventral stream of visual processing, including abolished grip scaling during memory-guided grasping and an inability to differentiate objects based on their shape. This research increases our understanding of the visuomotor deficits associated with PCA. It also adds to our knowledge of how visual information is processed in the brain, including the complex interaction between vision for action and vision for perception.

NEPTUNE Canada possesses a large collection of video data for monitoring marine life. Such data is important for marine biologists who can observe species in their natural habitat on a 24/7 basis. It is counterproductive for researchers to manually search for the events of interest (EOI) in a large database. Our study aims to perform the automatic detection of the EOI de ned as animal motion. The output of this approach is in a summary video clip of the original video fi le that contains all salient events with their associated start and end frames. Our work is based on Laptev [1] spatio-temporal interest points detection method. Interest points in the 3D spatio-temporal domain (x,y,t) require frame values in local spatio-temporal volumes to have large variations along all three dimensions. These local intensity variations are captured in the magnitude of the spatio-temporal derivatives. We report experimental results on video summarization using a database of videos from Neptune Canada. The eff ect of several parameters on the performance of the proposed approach is studied in detail. / Graduate

No description available.

No description available.

One of the fundamental problems in computer vision is the identification of objects from their shape. The research reported in this thesis is directed toward the development of a scheme for representing the shape of an object which allows it to be recognised both quickly and robustly across a wide range of viewing conditions. Given a shape described by a set of primitive elements, eg. straight line segments, the proposed scheme involves using a histogram to record the distribution of geometric features, eg. angle and distance, measured between pairs of primitives. This form of shape representation has a number advantages over previously proposed schemes. Foremost among these is the fact that it is able to produce local representations of shape, based on individual line segments. Recognition based on such representation is robust to the problems arising in cluttered scenes. Representations produced by the scheme are also invariant to certain object transformations, they degrade gracefully as the shape is fragmented and are strong enough to support discrimination between dissimilar objects. By treating the histogram recording a geometric feature distribution as a feature vector it is possible to match shapes using techniques from statistical pattern classification. This has the advantage that optimal matching accuracy can be achieved using processing which is both simple and uniform. The approach is therefore ideally suited to implementation in dedicated hardware. A detailed analysis is undertaken of the effect on recognition of changes in the description of a shape caused by fragmentation noise, scene clutter and sensor error. It is found that the properties of both the representation and matching components of the system combine to ensure that recognition is, in theory, unaffected by fragmentation noise, while it is maintained to very high levels of scene clutter. The factors which determine the effect of sensor error on the performance of the recognition system are fully analysed. The ability of the representational scheme to support object recognition is demonstrated in a number of different domains. The recognition of both 2D and 3D objects from a fixed viewpoint is demonstrated in conditions of severe fragmentation noise, occlusion and clutter. The scheme is then shown to extend straightforwardly to the representation of 3D shape. This is exploited to perform recognition and localisation of 3D objects from an arbitrary viewpoint, based on the matching of 3D scene and ,model shape descriptions. Finally, the use of the scheme within a multiple view-based approach to 3D object recognition is demonstrated.

No description available.

No description available.

No description available.

No description available.

No description available.