Visual Attention for Robotic Cognition: A Survey <s> III. COMPUTATIONAL MODELS OF VISUAL ATTENTION AND THE REQUIREMENTS OF ROBOTIC VISUAL ATTENTION <s> A number of psychophysical studies concerning the detection, localization and recognition of objects in the visual field have suggested a two-stage theory of human visual perception. The first stage is the “preattentive” mode, in which simple features are processed rapidly and in parallel over the entire visual field. In the second, “attentive” mode, a specialized processing focus, usually called the focus of attention, is directed to particular locations in the visual field. The analysis of complex forms and the recognition of objects are associated with this second stage.1 The computational justification for such a hypothesis comes from the realization that while it is possible to imagine specific algorithms performing tasks such as shape analysis and recognition at specific locations, it is difficult to imagine these algorithms operating in parallel over the whole visual scene, since such an approach will quickly lead to a combinatorial explosion in terms of required computational resources.2 This is essentially the major critique of Minsky and Papert to a universal application of perceptrons in visual perception.3 Taken together, these empirical and theoretical studies suggest that beyond a certain preprocessing stage, the analysis of visual information proceeds in a sequence of operations, each one applied to a selected location (or locations). <s> BIB001 </s> Visual Attention for Robotic Cognition: A Survey <s> III. COMPUTATIONAL MODELS OF VISUAL ATTENTION AND THE REQUIREMENTS OF ROBOTIC VISUAL ATTENTION <s> A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail. Index Terms—Visual attention, scene analysis, feature extraction, target detection, visual search. ———————— F ———————— <s> BIB002 </s> Visual Attention for Robotic Cognition: A Survey <s> III. COMPUTATIONAL MODELS OF VISUAL ATTENTION AND THE REQUIREMENTS OF ROBOTIC VISUAL ATTENTION <s> Most models of visual search, whether involving overt eye movements or covert shifts of attention, are based on the concept of a saliency map, that is, an explicit two-dimensional map that encodes the saliency or conspicuity of objects in the visual environment. Competition among neurons in this map gives rise to a single winning location that corresponds to the next attended target. Inhibiting this location automatically allows the system to attend to the next most salient location. We describe a detailed computer implementation of such a scheme, focusing on the problem of combining information across modalities, here orientation, intensity and color information, in a purely stimulus-driven manner. The model is applied to common psychophysical stimuli as well as to a very demanding visual search task. Its successful performance is used to address the extent to which the primate visual system carries out visual search via one or more such saliency maps and how this can be tested. <s> BIB003
Among the computational models of visual attention, the models proposed in computer vision gained widespread popularity in robotics. Specially, the computer vision models proposed in BIB001 , BIB002 ,  , and BIB003 have strong influence on the robotic model of visual attention. This is mostly because the strategies of computer vision models to analyze visual features make them suitable to be applied on the technical system (unlike the model proposed in computational neuroscience). Majority of the existing computer vision model, however, have some characteristics which impose some restrictions on their direct use for robotic visual attention. This section first summarizes the general properties of the computer vision models of visual attention and then sheds light on the issues that restrict their direct use in the robotic systems.