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Title: The role of object recognition in active vision : a computational study
Author: Cope, Alexander John
ISNI:       0000 0004 2742 5877
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2011
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Eye movements are essential to the way that primates and humans investigate the visual world. These eye movements depend upon the task being performed, and thus cannot be accounted for by bottom-up features, as in the model of Itti et al. (1998). Here we seek to investigate the role of task information in the redirection of gaze, by starting with an existing biologically based model of the primate oculomotor system (Chambers. 2(07). We integrate a revised version of the model with a new model of object recognition, produced using inspiration from the HMAX model of Riesenhuber and Poggio (1999), combined with a computational advantageous and biologically accurate method of visual attention. This approach of utilising and combining existing models where possible we describe as 'systems integration'. The full model reproduces a wealth of experimental evidence, in- cluding the effect of set size on reaction time for different difficulty visual search tasks (Treisman and Gelade, 1980), and additionally on saccadic latency and fixation duration for difficult visual search tasks (Motter and Belky, 1998a), as well as the effect of onset on search behavior found by Yantis and Jonides (1996). Novel explanations for these behaviours are suggested, under an overarching framework, which can only be provided because of 'the biological realism present in this model. We then extend the model with additional competencies, using an enhanced 'systems integration' approach. This involves including engineered phenomenological components that replicate neural competencies. This extended model is embodied in robotic hardware - thereby improving the veracity of the world-model interaction. The extended competencies include reward based associative learning, and habituation to repeated task irrelevant distraction. This model is exercised with an ethological experiment, and provides predictions regarding the nature of the mechanisms behind reward based as- sociative learning - notably, the model predicts that reversal learning is important when the reward associated with objects changes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available