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

Kunfeng Wang, Chao Gou, Nanning Zheng, James M. Rehg, Fei Yue Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)299-329
Number of pages31
JournalArtificial Intelligence Review
Issue number3
StatePublished - Oct 1 2017
Externally publishedYes


  • ACP Methodology
  • Complex scenes
  • Computer graphics
  • Image synthesis
  • Parallel Vision
  • Visual perception

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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