NDLI: Emergent algorithms for centroid and orientation detection in high-performance embedded cameras

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Emergent algorithms for centroid and orientation detection in high-performance embedded cameras

Content Provider	ACM Digital Library
Author	Kröller, Alexander Fey, Dietmar Komann, Marcus Fekete, Sándor P. Schmidt, Christiane
Abstract	Due to increasing speed and capabilities of production machines, the need for extremely fast and robust observation, classification, and error handling is vital to industrial image processing. We present an emergent algorithmic computing scheme and a corresponding embedded massively-parallel hardware architecture for these problems. They offer the potential to turn CMOS-camera-chips into intelligent vision devices which carry out tasks without help of a central processor, only based on local interaction of agents crawling on a large field of processing elements. It also constitutes a breakthrough for understanding sensor devices as a decentralized concept, resulting in much faster computation evading communication bottlenecks of classic approaches that become an ever-growing impediment to scalability. Here, in contrast, the number of agents and the field size and thus the computable image resolution is extremely scalable and therefore promises even more benefit with future hardware development. The results are based on novel algorithmic solutions allowing processor elements to compute center points, moments, and orientation of multiple image objects in parallel, which is of central importance to e.g. robotics. We finally present the algorithm's capabilities if realized in state-of-the-art FPGAs and ASICs.
Starting Page	221
Ending Page	230
Page Count	10
File Format	PDF
ISBN	9781605580777
DOI	10.1145/1366230.1366271
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-05-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Image processing Massively-parallel Object detection Emergent algorithms Marching pixels
Content Type	Text
Resource Type	Article

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