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Visual attention is the cognitive process of selectively focusing on certain areas of a visual scene while ignoring the others. It is a desirable capability for intelligent video surveillance systems, as it allows them to control the aim of mobile cameras or to selectively process the most relevant parts of the captured images. This paper proposes an adaptation of a well-known biologicallyinspired visual attention model in order to increase its computational efficiency without sacrificing its accuracy, and shows that the latter can be further improved through a supervised training stage that fine-tunes the model to the particular application scope in which the system is being utilized. Experimental results and comparisons with previous visual attention techniques are shown and discussed.

No abstract

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractThis paper presents a new trend within the Authoring744 research initiative: the creation of 3D humanoids. Authoring744 proposes the creation of content using descriptions that drive content synthesis. Here we present the first steps towards the generation of 3D humanoids and their future animation within the Authoring744 framework, which proposes to use the MPEG-7 and MPEG-4 standards for, respectively, semantically describing and representing content. One of the main objectives is the creation of more intuitive and easier to use 3D content authoring tools.