Brain-inspired algorithms for processing of visual data
It provides a synthesis of neuro-scientific insights for researchers in image processing, but is incremental as it reviews existing approaches.
This paper reviews brain-inspired computational models for image processing and computer vision, analyzing connections between the brain's visual cortex and Convolutional Networks, with a focus on inhibition mechanisms for improved stability.
The study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing. In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.