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Title: Search and attention for machine vision
Author: Brohan, Kevin Patrick
ISNI:       0000 0004 2723 7534
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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This thesis addresses the generation of behaviourally useful, robust representations of the sensory world in the context of machine vision and behaviour. The goals of the work presented in this thesis are to investigate strategies for representing the visual world in a way which is behaviourally useful, to investigate the use of a neurally inspired early perceptual organisation system upon high-level processing in an object recognition system and to investigate the use of a perceptual organisation system on driving an object-based selection process. To address these problems, a biologically inspired framework for machine attention has been developed at a high level of neural abstraction, which has been heavily inspired by the psychological and physiological literature. The framework is described in this thesis, and three system implementations, which investigate the above issues, are described and analysed in detail. The primate brain has access to a coherent representation of the external world, which appears as objects at different spatial locations. It is through these representations that appropriate behavioural responses may be generated. For example, we do not become confused by cluttered scenes or by occluded objects. The representation of the visual scene is generated in a hierarchical computing structure in the primate brain: while shape and position information are able to drive attentional selection rapidly, high-level processes such as object recognition must be performed serially, passing through an attentional bottleneck. Through the process of attentional selection, the primate visual system identifies behaviourally relevant regions of the visual scene, which allows it to prioritise serial attentional shifts towards certain locations. In primates, the process of attentional selection is complex, operating upon surface representations which are robust to occlusion. Attention itself suppresses neural activity related to distractor objects, while sustaining activity relating to the target, allowing the target object to have a clear neural representation upon which the recognition process can operate. This thesis concludes that dynamic representations that are both early and robust against occlusion have the potential to be highly useful in machine vision and behaviour applications.
Supervisor: Dudek, Piotr. Sponsor: EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Machine Vision ; Visual Search ; Occlusion ; Object Recognition ; Perceptual Organisation ; Shape-Based Guidance