DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision - Info and Reading Options
By Defense Technical Information Center
"DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision" and the language of the book is English.
“DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision” Metadata:
- Title: ➤ DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision
- Author: ➤ Defense Technical Information Center
- Language: English
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- Internet Archive ID: DTIC_ADA247427
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"DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision" Description:
The Internet Archive:
Animate vision systems couple computer vision and robotics to achieve robust and accurate vision, as well as other complex behavior. These systems combine low-level sensory processing and effector output with high-level cognitive planning - all computationally intensive tasks that can benefit from parallel processing. No single model of parallel programming is likely to serve for all tasks, however. Early vision algorithms are intensely data parallel, often utilizing fine-grain parallel computations that share an image, while cognition algorithms decompose naturally by function, often consisting of loosely-coupled, coarse-grain parallel units. A typical animate vision application will likely consist of many tasks, each of which may require a different parallel programming model, and all of which must cooperate to achieve the desired behavior. These multi-model programs require an underlying software system that not only supports several different models of parallel computation simultaneously, but which also allows tasks implemented in different models to interact.
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"DTIC ADA247427: The Rochester Checkers Player: Multi-Model Parallel Programming For Animate Vision" is available for download from The Internet Archive in "texts" format, the size of the file-s is: 19.95 Mbs, and the file-s went public at Mon Mar 05 2018.
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