Abstract
In this paper a consistent and efficient but yet convenient system for parallel computer vision, and in fact also realtime actuator control is proposed. The system implements the multi-agent paradigm and a blackboard information storage. This, in combination with a generic interface for hardware abstraction and integration of external software components, is setup on basis of the message passing interface (MPI). The system allows for data- and task-parallel processing, and supports both synchronous communication, as data exchange can be triggered by events, and asynchronous communication, as data can be polled, strategies. Also, by duplication of processing units (agents) redundant processing is possible to achieve greater robustness. As the system automatically distributes the task units to available resources, and a monitoring concept allows for combination of tasks and their composition to complex processes, it is easy to develop efficient parallel vision / robotics applications quickly. Multiple vision based applications have already been implemented, including academic, research related fields and prototypes for industrial automation. For the scientific community the system has been recently launched open-source.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE-IS and T Electronic Imaging - Parallel Processing for Imaging Applications |
| DOIs | |
| State | Published - 2011 |
| Event | Parallel Processing for Imaging Applications - San Francisco, CA, United States Duration: 24 Jan 2011 → 25 Jan 2011 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 7872 |
| ISSN (Print) | 0277-786X |
Conference
| Conference | Parallel Processing for Imaging Applications |
|---|---|
| Country/Territory | United States |
| City | San Francisco, CA |
| Period | 24/01/11 → 25/01/11 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
Keywords
- Computing Clusters
- Open-Source Framework
- Parallel Computer Vision
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