Artificial Intelligence
Computational models of visual processing
Computational models of visual processing
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
Promising directions in active vision
International Journal of Computer Vision
Early vision and beyond
The psychophysics of texture segmentation
Early vision and beyond
A brief overview of texture processing in machine vision
Early vision and beyond
Two-dimensional and three-dimensional texture processing in visual cortex of the macaque monkey
Early vision and beyond
Attention to surfaces: beyond a Cartesian understanding of focal attention
Early vision and beyond
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Likelihood Strategy for Directing Attention during Visual Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention
Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention
Attentional sequence-based recognition: Markovian and evidential reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Attention-based video streaming
Image Communication
Bubble space and place representation in topological maps
International Journal of Robotics Research
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The aim of this paper is to develop a rich set of visual primitives that can be used by a camera-endowed robot as it is exploring a scene and thus generating an attentional sequence--spatio-temporally related sets of visual features. Our starting point is inspired by the work of Gallant et al. on the area V4 response of the macaque monkeys to Cartesian and non-Cartesian stimuli. The novelty of these stimuli is that in addition to the conventional sinusoidal gratings, they also include non-Cartesian stimuli such as circular, polar and hyperbolic gratings. Based on this stimulus set and introducing frequency as a parameter, we obtain a rich set of visual primitives. These visual primitives are biologically motivated, nearly orthogonal with some degree of redundancy, can be made complete as required and yet implementable on off-the-shelf hardware for real-time selective vision-robot applications. Attentional sequences are then formed as a spatio-temporal sequence of observations--each of which encodes the filter responses of each fovea as an observation vector consisting of responses of 50 filters. A series of experiments serve to demonstrate the use of these visual primitives in attention-based real-life scene recognition tasks: (1) modeling complex scenes based on average attentional sequence responses and (2) fast real-time recognition of relatively complex scenes with a few saccades-- based on the comparison of the current attentional sequence to the a priori learned average observation vectors.