There is a large growth in hardware and software systems capable of producing vast amounts of image and video data. These systems are rich sources of continuous and possibly infinite image and video streams. This motivates researchers to build scalable computer vision systems that utilize data-streaming concepts for large-scale processing of visual data streams. We currently lack formal and scalable mechanisms and frameworks for building and optimizing large-scale visual processing. To address this issue we present a formal algebra framework for the mathematical description of computer vision pipelines for processing image and video streams. The algebra defines a set of abstract and concurrent operators with well-defined semantics for building scalable computer vision systems. It naturally describes feedback control and provides a formal and abstract method for data-stream manipulation, adaptive parameter selection, dynamic reconfiguration, incremental optimization, and defining common optimization and cost models.
We demonstrate the efficacy of this algebra by implementing state-of-the-art optical flow and road boundary detection systems using this algebra.
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