Custom CMOS vision sensors could offer large opportunities for ultra-low power applications, introducing novel visual computation paradigms, aimed at closing the large gap between vision technology and energy-autonomous sensory systems. Energy-aware vision could offer new opportunities to all those applications, such as security, safety, environmental monitoring and many others, where communication infrastructures and power supply are not available or too expensive to be provided.
This thesis aims at demonstrating this concept, exploiting the potential of an energy-aware vision sensor, developed at FBK, that extracts the spatial contrast and delivers compressed data. As a case study, a custom stereo-vision algorithm has been developed, taking advantage of the sensor characteristics, targeted to a lower complexity and reduced memory with respect to a standard stereo-vision processing. Under specific conditions, the proposed approach has proven to be very promising, although much work has still to be done both at sensor and at processing levels.The last part of this thesis is focused on the improvement of the custom sensor. A novel vision sensor architecture has been developed, which is based on a proprietary algorithm, developed by a partner of FBK and targeted to surveillance applications. The algorithm is based on adaptive temporal contrast extraction and is very suitable to be implemented at chip level. Although the output of the algorithm has strong similarities with the spatial contrast vision sensor, it relies on temporal contrast rather than spatial one, which is much more robust for event detection applications. A first prototype of ultra-low power adaptive temporal contrast vision sensor has been developed and tested.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/367662 |
| Date | January 2012 |
| Creators | Cottini, Nicola |
| Contributors | Cottini, Nicola |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:95, numberofpages:95 |
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