Dynamic Visual Attention: competitive versus motion priority scheme

Defined as attentive process in presence of visual sequences, dynamic visual attention responds to static and motion features as well. For a computer model, a straightforward way to integrate these features is to combine all features in a competitive scheme: the saliency map contains a contribution of each feature, static and motion. Another way of integration is to combine the features in a motion priority scheme: in presence of motion, the saliency map is computed as the motion map, and in absence of motion, as the static map. In this paper, four models are considered: two models based on a competitive scheme and two models based on a motion priority scheme. The models are evaluated experimentally by comparing them with respect to the eye movement patterns of human subjects, while viewing a set of video sequences. Qualitative and quantitative evaluations, performed in the context of simple synthetic video sequences, show the highest performance of the motion priority scheme, compared to the competitive scheme

Dynamic Visual Attention: competitive versus motion priority scheme

Defined as attentive process in presence of visual sequences, dynamic visual attention responds to static and motion features as well. For a computer model, a straightforward way to integrate these features is to combine all features in a competitive scheme: the saliency map contains a contribution of each feature, static and motion. Another way of integration is to combine the features in a motion priority scheme: in presence of motion, the saliency map is computed as the motion map, and in absence of motion, as the static map. In this paper, four models are considered: two models based on a competitive scheme and two models based on a motion priority scheme. The models are evaluated experimentally by comparing them with respect to the eye movement patterns of human subjects, while viewing a set of video sequences. Qualitative and quantitative evaluations, performed in the context of simple synthetic video sequences, show the highest performance of the motion priority scheme, compared to the competitive scheme

Near IR spectroscopy for the characterization of dispersion in polymer-clay nanocomposites

© 2015 Wiley-VCH Verlag GmbH & Co. KGaA. Since their potential has become widely recognized, one of the major research lines on polymer-clay nanocomposites has focused on the preparation of well-dispersed systems. Currently, there is an understanding that a high degree of dispersion, particularly exfoliation, of the nanoclay is required to improve the overall performance. Twin screw extrusion not only allows for control of the main variables (shear, stress and time), but also, due to the typical modular construction, offers a high degree of freedom in creating the adequate screw design and enables knowledgeable alteration of the barrel. These features offer a solid basis for the development and implementation of apt in-line/on-line monitoring techniques, able to follow up the evolution of dispersion of polymer-clay nanocomposites during processing. This chapter includs the validation, implementation, and application of a methodology based on inline near-infrared (NIR) spectroscopy for the characterization of the dispersion along the extruder axis. Overall, the results obtained confirm that NIR is a valid tool for the on-line characterization of these materials, offering the possibility of assessing in real time the clay dispersion, enabling proper corrective and/or optimization actions over the material characteristics in a timely manner.(undefined