Cellular automata-based optical flow computation for "just-in-time" applications

Adorni, Giovanni; Cagnoni, Stefano; Mordonini, Monica

doi:10.1109/iciap.1999.797664

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…http://jivp.eurasipjournals.com/content/2014/1 /18 There have been many alternatives and they have evolved along with the technology. In some cases, SIMD processor arrays with specific chips, either existing [36] or designed ad hoc for the computation of optical flow [13,17,[37][38][39], have been used. General-purpose MIMD as the connection machine [40,41], networks of transputers [42], or cellular neural networks [43,44] were also used in the past.…”

Section: Parallelization Of the Optical Flowmentioning

confidence: 99%

Parallelization of the optical flow computation in sequences from moving cameras

et al. 2014

View full text Add to dashboard Cite

This paper presents a flexible and scalable approach to the parallelization of the computation of optical flow. This approach is based on data parallel distribution. Images are divided into several subimages processed by a software pipeline while respecting dependencies between computation stages. The parallelization has been implemented in three different infrastructures: shared, distributed memory, and hybrid to show its conceptual flexibility and scalability. A significant improvement in performance was obtained in all three cases. These versions have been used to compute the optical flow of video sequences taken in adverse conditions, with a moving camera and natural-light conditions, on board a conventional vehicle traveling on public roads. The parallelization adopted has been developed from the analysis of dependencies presented by the well-known Lucas-Kanade algorithm, using a sequential version developed at the University of Porto as the starting point.

show abstract