Snap & Play

In this article, by taking a popular game, the Find-the-Difference (FiDi) game, as a concrete example, we explore how state-of-the-art image processing techniques can assist in developing a personalized, automatic, and dynamic game. Unlike the traditional FiDi game, where image pairs (source image and target image) with five different patches are manually produced by professional game developers, the proposed Personalized FiDi (P-FiDi) electronic game can be played in a fully automatic Snap & Play mode. Snap means that players first take photos with their digital cameras. The newly captured photos are used as source images and fed into the P-FiDi system to autogenerate the counterpart target images for users to play. Four steps are adopted to autogenerate target images: enhancing the visual quality of source images, extracting some changeable patches from the source image, selecting the most suitable combination of changeable patches and difference styles for the image, and generating the differences on the target image with state-of-the-art image processing techniques. In addition, the P-FiDi game can be easily redesigned for the im-game advertising. Extensive experiments show that the P-FiDi electronic game is satisfying in terms of player experience, seamless advertisement, and technical feasibility.

Authors

• Si Liu Beihang University
• Qiang Chen Tencent WeChat
• Shuicheng Yan Skywork AI, Singapore Nanyang Technological University, Singapore
• Changsheng Xu MAIS, Institute of Automation, Chinese Academy of Sciences Pengcheng Laboratory School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)
• Hanqing Lu Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, 100049, Beijing, China

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