Alcides Xavier Benicasa

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There are several real situations in which it is useful to have a system able to detect a specific target or a salient object and its localization in a given image in autonomous way. To guide the attention based on known characteristics of an object and primitive information of the image is not a trivial task for visual attention. Several works about visual(More)
Analysis and recognition of objects in complex scenes is a demanding task for a computer. There is a selection mechanism, named visual attention, that optimizes the visual system, in which only the important parts of the scene are considered at a time. In this work, an object-based visual attention model with both bottom-up and top-down modulation is(More)
Research in qualitative models of visual attention has mainly focused on the bottom-up guidance of early visual features. Here we propose a new model which combine both bottom-up and top-down modulation into the visual selection model. The proposed model is composed of five main components: a Visual Feature Extraction module, a LEGION network for image(More)
There are several real situations in which it is useful to have a system able to detect a salient object and its localization in a given scene in autonomous way. Among these systems are robot vision systems that must detect determined objects, security systems, that must detect stranger people or objects in environment. These systems need to present a very(More)
In this paper, a new visual selection model is proposed, which combines both early visual features and object-based visual selection modulations. This model integrates three main mechanisms. The first is responsible for the segmentation of the scene allowing the identification of objects. In the second one, the average of saliency of each object is(More)
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