2 research outputs found
Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
Findings in recent years on the sensitivity of convolutional neural networks
to additive noise, light conditions and to the wholeness of the training
dataset, indicate that this technology still lacks the robustness needed for
the autonomous robotic industry. In an attempt to bring computer vision
algorithms closer to the capabilities of a human operator, the mechanisms of
the human visual system was analyzed in this work. Recent studies show that the
mechanisms behind the recognition process in the human brain include continuous
generation of predictions based on prior knowledge of the world. These
predictions enable rapid generation of contextual hypotheses that bias the
outcome of the recognition process. This mechanism is especially advantageous
in situations of uncertainty, when visual input is ambiguous. In addition, the
human visual system continuously updates its knowledge about the world based on
the gaps between its prediction and the visual feedback. Convolutional neural
networks are feed forward in nature and lack such top-down contextual
attenuation mechanisms. As a result, although they process massive amounts of
visual information during their operation, the information is not transformed
into knowledge that can be used to generate contextual predictions and improve
their performance. In this work, an architecture was designed that aims to
integrate the concepts behind the top-down prediction and learning processes of
the human visual system with the state of the art bottom-up object recognition
models, e.g., deep convolutional neural networks. The work focuses on two
mechanisms of the human visual system: anticipation-driven perception and
reinforcement-driven learning. Imitating these top-down mechanisms, together
with the state of the art bottom-up feed-forward algorithms, resulted in an
accurate, robust, and continuously improving target recognition model