AUTONOMOUS CAMERA CONTROL BY NEURAL MODELS IN ROBOTIC VISION SYSTEMS
Tyler W. Garaas, Frank Marino, Marc Pomplun · 2009
Recently there has been growing interest in creating large-scale simulations of certain areas in the brain. The areas that are receiving the overwhelming focus are visual in nature, which may provide a means to compute some of the complex visual functions that have plagued AI researchers for many decades; robust object recognition, for example. Additionally, with the recent introduction of cheap computational hardware capable of computing at several teraflops, real-time robotic vision systems will likely be implemented using simplified neural models based on their slower, more realistic counterparts. This paper presents a series of small neural networks that can be integrated into a neural model of the human retina to automatically control the white-balance and exposure parameters of a standard video camera to optimize the computational processing performed by the neural model. Results of a sample implementation including a comparison with proprietary methods are presented. One strong advantage that these integrated subnetworks possess over proprietary mechanisms is that ‘attention ’ signals could be used to selectively optimize areas of the image that are most relevant to the task at hand. 1