Abstract
Computational visual attention systems have been constructed in order for robots and other devices to detect and locate regions of interest in their visual world. Such systems often attempt to take account of what is known of the human visual system and employ concepts, such as ‘active vision’, to gain various perceived advantages. However, despite the potential for gaining insights from such experiments, the computational requirements for visual attention processing are often not clearly presented from a biological perspective. This was the primary objective of this study, attained through two specific phases of investigation: 1) conceptual modeling of a top-down-bottom-up framework through critical analysis of the psychophysical and neurophysiological literature, 2) implementation and validation of the model into robotic hardware (as a representative of an active vision system). Seven computational requirements were identified: 1) transformation of retinotopic to egocentric mappings, 2) spatial memory for the purposes of medium-term inhibition of return, 3) synchronization of ‘where’ and ‘what’ information from the two visual streams, 4) convergence of top-down and bottom-up information to a centralized point of information processing, 5) a threshold function to elicit saccade action, 6) a function to represent task relevance as a ratio of excitation and inhibition, and 7) derivation of excitation and inhibition values from object-associated feature classes. The model provides further insight into the nature of data representation and transfer between brain regions associated with the vertebrate ‘active’ visual attention system. In particular, the model lends strong support to the functional role of the lateral intraparietal region of the brain as a primary area of information consolidation that directs putative action through the use of a ‘priority map’.
Highlights
One of the main problems in trying to define the underlying mechanisms of visual attention is that its neurophysiological drive stems from several sources, the weights of which are determined by different contextual paradigms
Despite the potential for gaining biological insights from such experiments, the computational requirements for visual attention processing are often not clearly presented from a biological perspective. This was the primary objective of this study, to be attained through two specific phases of investigation: 1) conceptual modeling of a top-down-bottom-up framework through critical analysis of the psychophysical and neurophysiological literature to predict first stage computational requirements, 2) implementation of the model into robotic hardware as a representative of an active vision system and validation through behavioural testing to subsequently identify second stage computational requirements
The dorsal pathway passes through V1, V2 and V5 arriving at the posterior parietal cortex and is considered to process and hold spatial information about objects
Summary
One of the main problems in trying to define the underlying mechanisms of visual attention is that its neurophysiological drive stems from several sources, the weights of which are determined by different contextual paradigms. For example, can be in the context of ‘‘searching’’ or ‘‘not searching’’, different levels of task-driven information, different levels of task-relevant and object feature complexities or, prior experience or naivety to the visual scene. When ‘‘not searching’’, visual attention is driven predominantly by in-built saliency filters present in the early stages of visual processing within the visual cortex. For each saccade the p value was logged along with its corresponding features class Out of these data were derived the absolute number of saccades, total fixation time and the average fixation time for each object present. Four balls were placed on the table (two red and two blue) and the excitation and inhibition values were pre-defined for each colour class
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