平行视觉:基于ACP的智能视觉计算方法

在视觉计算研究中，对复杂环境的适应能力通常决定了算法能否实际应用，已经成为该领域的研究焦点之一.由人工社会（Artificial societies）、计算实验（Computational experiments）、平行执行（Parallel execution）构成的ACP理论在复杂系统建模与调控中发挥着重要作用.本文将ACP理论引入智能视觉计算领域，提出平行视觉的基本框架与关键技术.平行视觉利用人工场景来模拟和表示复杂挑战的实际场景，通过计算实验进行各种视觉模型的训练与评估，最后借助平行执行来在线优化视觉系统，实现对复杂环境的智能感知与理解.这一虚实互动的视觉计算方法结合了计算机图形学、虚拟现实、机器学习、知识自动化等技术，是视觉系统走向应用的有效途径和自然选择.

Parallel Vision: An ACP-based Approach to Intelligent Vision Computing

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.