Modeling Object Dissimilarity for Deep Saliency Prediction
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In this paper we present a computational model of dynamic visual attention on the sphere which combines static (intensity,chromaticity, orientation) and motion features in order to detect salient locations in omnidirectional image sequences while working d ...
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We address the problem of recognizing, in dynamic meetings in which people do not remain seated all the time, the visual focus of attention (VFOA) of seated people from their head pose and contextual activity cues. We propose a model that comprises the VFO ...
The processing and perception of visual stimuli are greatly dependent on the context in which those stimuli are presented. While this is not always (and indeed is probably not "supposed" to be) evident when viewing real-world scenes, the abundance of conte ...
We address the problem of recognizing, in dynamic meetings in which people do not remain seated all the time, the visual focus of attention (VFOA) of seated people from their head pose and contextual activity cues. We propose a model that comprises the VFO ...
Visual attention models mimic the ability of a visual system, to detect potentially relevant parts of a scene. This process of attentional selection is a prerequisite for higher level tasks such as object recognition. Given the high relevance of temporal a ...
Defined as an attentive process in the context of visual sequences, dynamic visual attention refers to the selection of the most informative parts of video sequence. This paper investigates the contribution of motion in dynamic visual attention, and specif ...
Visual attention, defined as the ability of a biological or artificial vision system to rapidly detect potentially relevant parts of a visual scene, provides a general purpose solution for low level feature detection in a vision architecture. Well consider ...
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