Contextual Classification on a CDC Flexible Processor System
1981
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage146
- Downloads93
- Abstract Views53
Article Description
Contextual classifiers are being developed to exploit spatial/spectral content of a pixel to achieve higher classification accuracy. Contextual classification requires large amounts of computation, so special hardware is of value. One parallel processing system designed for image processing is the CDC Flexible Processor Array, the basis for the discussion in this paper. A simulator for the CDC Flexible Processor Array has been developed for program testing, debugging and timing. The simulated timings are presented in this paper. For comparison, the same algorithms have been run on a PDP-11/70. These timings are analyzed and discussed for context neighborhoods of size three and nine.
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