Optimization and implementation of bio-inspired feature extraction frameworks for visual object recognition

Industry has growing needs for so-called \intelligent systems", capable of not only acquire data, but also to analyse it and to make decisions accordingly. Such systems are particularly useful for video-surveillance, in which case alarms must be raised in case of an intrusion. For cost saving and power consumption reasons, it is better to perform that process as close to the sensor as possible. To address that issue, a promising approach is to use bio-inspired frameworks, which consist in applying computational biology models to industrial applications. The work carried out during that thesis consisted in selecting bio-inspired feature extraction frameworks, and to optimize them with the aim to implement them on a dedicated hardware platform, for computer vision applications. First, we propose a generic algorithm, which may be used in several use case scenarios, having an acceptable complexity and a low memory print. Then, we proposed optimizations for a more global framework, based on precision degradation in computations, hence easing up its implementation on embedded systems. Results suggest that while the framework we developed may not be as accurate as the state of the art, it is more generic. Furthermore, the optimizations we proposed for the more complex framework are fully compatible with other optimizations from the literature, and provide encouraging perspective for future developments. Finally, both contributions have a scope that goes beyond the sole frameworks that we studied, and may be used in other, more widely used frameworks as well.