Computational models of visual processes are of interest in fields such as cybernetics, robotics, computer vision and others. This thesis provides an analysis of a model of attention and of intermediate representation layers in the visual cortex that have direct impact on the next generation of object recognition strategies in computer vision. Biological inspiration - and even biological realism - is currently of great interest in the computer vision community. This thesis includes three major pieces, explained next.;Following previous authors such as [Zucker, 1981] and [Marr, 1982], I have shown that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems.;First, I believe that visual attention is a requirement to perform non-detection object recognition tasks. In order to test this hypothesis we compare the Selective Tuning model of attention [Tsotsos et al., 1995] to studies from psychophysics in visual search tasks involving color and 2D shapes. Second, I define a biologically plausible model of Shape Representation which incorporates intermediate layers of visual representation that have not previously been fully explored. I hypothesize that endstopping and curvature cells are of great importance for shape selectivity and show how their combination can lead to shape selective neurons. This Shape Representation model provides a highly accurate fit with neural data from [Pasupathy and Connor, 2001, Pasupathy and Connor, 2002]. Finally, in the same way curvature parts may be configured into shapes, spatial gradients of velocity vectors may be related to optic flow in a hierarchical representation of visual motion analysis. For my last contribution I provide psychophysical evidence of the role of spatial gradients of velocity in optical flow perception as well as neurophysiological evidence for neurons tuned for such gradients.
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