Attention Based Object Recogniton Applied to a Humanoid Robot

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 applied to the humanoid robot NAO to allow a new attention procedure to the robot. This means that the robot, by using its cameras, can recognize geometric figures even with the competition for the attention of all the objects in the image in real time. The proposed method is validated through some tests with 13 to 14 year old kids interacting with the robot NAO that provides some tips (such as the perimeter and area calculation formulas) and recognizes the figure showed by these children. The results are very promissor and show that the proposed approach can contribute for inserting robotics in the educacional context.

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