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Learning visual object detection and localisation using icVision
2013
Biologically Inspired Cognitive Architectures
We present a framework combining computer vision and machine learning for the learning of object recognition in humanoid robots. A biologically inspired, bottom-up architecture is introduced to facilitate visual perception and cognitve robotics research. A number of experiments with this icVision framework are described. We showcase both detection and identification in the image plane (2D), using machine learning. In addition we show how a biologically inspired attention mechanism allows for
doi:10.1016/j.bica.2013.05.009
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