Co-optimization of a bio-inspired image sensor and scene analysing technics
Published : 8 November 2016
Artificial vision systems (camera(s) and processor(s)) have recognition capabilities well below those achieved by biological systems (eye – cortex). Moreover, biological systems are able to process information within a few milliseconds, which is still out of range of electronic systems, even though their electronic image sensors are far from achieving the resolution of human eyes (few dozen megapixel against more than one hundred million). This thesis aims at addressing the challenge posed by the biology by designing integrated bio-inspired sensor architectures. Our approach is based three assumptions: first, resolution biological imaging sensors is not uniform, the best resolved zone (the fovea) is dedicated to the acquisition of the areas of interest of the scene; secondly, pre-processing from the sensor are used to compress the information; finally, the processing of information is context and prior knowledge dependent. This exploratory thesis, aims to devise, within the frame of these hypothesis, breakthrough solutions with respect to the state of the art, to endow autonomous artificial systems (drones of all kinds (UAV, UGV, …), machine tools, smart camera) of ability to perception of their complex environment, while using only limited resources, i.e. those of embedded systems. The candidate should have strong tastes or skills in image processing and digital architectures.