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Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

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Abstract

In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61533019 and No. 71232006).

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Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Kunfeng Wang, Chao Gou & Fei-Yue Wang

  2. Institute of Artificial Intelligence and Robotics (IAIR), Xi’an Jiaotong University, Xi’an, 710049, China

    Nanning Zheng

  3. School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    James M. Rehg

  4. Research Center for Computational Experiments and Parallel Systems Technology, National University of Defense Technology, Changsha, 410073, China

    Fei-Yue Wang

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Wang, K., Gou, C., Zheng, N. et al. Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives. Artif Intell Rev 48, 299–329 (2017). https://doi.org/10.1007/s10462-017-9569-z

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  • DOI: https://doi.org/10.1007/s10462-017-9569-z

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