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Cognitive aspects of multiple tasks and perception: Overview and discussion on recent studies

机译:多项任务和感知的认知方面:最近研究的概述和讨论

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Cognitive aspects implemented in computational processes establish frames to model human actions relative to the human corresponding senses. Vision is one of them. To conceive appropriated artificial architectures, behavior manifestations and brain structural proprieties are intensively studied. Physiology is modeled in simplified schemas in order to be implemented in virtual systems and machine models. Eye tracking models offer clues for implementing artificial vision on robots, to give salient information for marketing, for art, or forensic research. If the task might be relatively simple for following one precise target in an image, the issues are more complicated when more objects have to be identified, in a multitask perception simulation. Another aspect is to take into consideration the semantic content and this depends on the detailed, rule-frames, formulation of the problem. A number of direct and reverse processes are identified: from real image to human vision and understanding, and from observation of the human behavior, to computer modeling and image processing. The paper makes a quick overview on the relevant studies connected to these topics and proposes a new schema to identify salient points in an image, based both on image processing techniques and on cognitive aspects, mainly from vision and multi-task perception studies.
机译:在计算过程中实现的认知方面建立了框架,以相对于人类相应的感官对人类行为进行建模。视觉就是其中之一。为了构想适当的人造建筑,人们对行为表现和大脑结构特性进行了深入研究。生理以简化的模式建模,以便在虚拟系统和机器模型中实现。眼动追踪模型提供了在机器人上实施人工视觉的线索,可为营销,艺术或法医研究提供重要信息。如果对于跟踪图像中的一个精确目标而言任务可能相对简单,那么在多任务感知模拟中,当必须识别更多对象时,问题就更加复杂。另一个方面是要考虑语义内容,这取决于问题的详细规则框架。确定了许多直接和反向过程:从真实图像到人类的视觉和理解,从人类行为的观察到计算机建模和图像处理。本文简要概述了与这些主题相关的相关研究,并提出了一种新方案,该方案可基于图像处理技术和认知方面(主要来自视觉和多任务感知研究)来识别图像中的显着点。

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