|王坤峰; 苟超; 王飞跃|
; In vision computing, the adaptability of an algorithm to complex environments often determines whether it is able to work in the real world. This issue has become a focus of recent vision computing research. Currently, the ACP theory that comprises artificial societies, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper introduces the ACP theory into the vision computing field, and proposes parallel vision and its basic framework and key techniques. For parallel vision, photo-realistic artificial scenes are used to model and represent complex real scenes, computational experiments are utilized to train and evaluate a variety of visual models, and parallel execution is conducted to optimize the vision system and achieve perception and understanding of complex environments. This virtual/real interactive vision computing approach integrates many technologies including computer graphics, virtual reality, machine learning, and knowledge automation, and is developing towards practically effective vision systems.
|Keyword||Parallel Vision Complex Environments Acp Theory Data-driven Virtual/real Interaction|
|王坤峰,苟超,王飞跃. 平行视觉:基于ACP的智能视觉计算方法[J]. 自动化学报,2016,42(10):1490-1500.|
|MLA||王坤峰,et al."平行视觉:基于ACP的智能视觉计算方法".自动化学报 42.10(2016):1490-1500.|
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|平行视觉_基于ACP的智能视觉计算方法.（6888KB）||期刊论文||作者接受稿||开放获取||CC BY-NC-SA||View Download|
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