Making real games virtual: Tracking board game pieces

Scher, Steve; Crabb, R.; Davis, Jason V.

doi:10.1109/icpr.2008.4761307

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Mathematicians, computer scientists, and engineers have attempted to model, analyze, and predict Go moves [2], [6], [12], [13], [14]. Others have automatically extracted Go games from still images and video streams [9], [10], [11]. What distinguishes the AutoGoRecorder is its simplicity and portability.…”

Section: Related Workmentioning

confidence: 99%

An application for the game of Go: Automatic live Go recording and searchable Go database

Srisuphab

Silapachote

2012

TENCON 2012 IEEE Region 10 Conference

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The key to become a skillful Go player is to learn from experience, analyzing and reflecting on previously played games. Rich in strategy, the game of Go requires a player to recognize winning or losing configurations of game pieces on the board. Knowing how to respond is essential, and training to do so requires an extensive study of various different tactics used by a diverse group of players, especially professionals.

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Section: Related Workmentioning

confidence: 99%

An application for the game of Go: Automatic live Go recording and searchable Go database

Srisuphab

Silapachote

2012

TENCON 2012 IEEE Region 10 Conference

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“…However, they report neither qualitative nor quantitative assessment of game-state reconstruction accuracy. Scher et al [19] present a method for recovering a sequence of GOgame moves from human-view image sequences. A novel aspect of this work is that they improve upon the purely vision-based game-state reconstruction by incorporating the rules of GO as constraints.…”

Section: B Game-state Reconstruction From Visual Inputmentioning

confidence: 99%

Learning physically-instantiated game play through visual observation

Barbu

Narayanaswamy

Siskind

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-We present an integrated vision and robotic system that plays, and learns to play, simple physically-instantiated board games that are variants of TIC TAC TOE and HEXA-PAWN. We employ novel custom vision and robotic hardware designed specifically for this learning task. The game rules can be parametrically specified. Two independent computational agents alternate playing the two opponents with the shared vision and robotic hardware, using pre-specified rule sets. A third independent computational agent, sharing the same hardware, learns the game rules solely by observing the physical play, without access to the pre-specified rule set, using inductive logic programming with minimal background knowledge possessed by human children. The vision component of our integrated system reliably detects the position of the board in the image and reconstructs the game state after every move, from a single image. The robotic component reliably moves pieces both between board positions and to and from off-board positions as needed by an arbitrary parametrically-specified legal-move generator. Thus the rules of games learned solely by observing physical play can drive further physical play. We demonstrate our system learning to play six different games.

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A Robust Algorithm for Go Image Recognition in Go Game

Song

2018

2018 IEEE 4th International Conference on Computer and Communications (ICCC)

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Making real games virtual: Tracking board game pieces

Cited by 3 publications

References 4 publications

An application for the game of Go: Automatic live Go recording and searchable Go database

An application for the game of Go: Automatic live Go recording and searchable Go database

Learning physically-instantiated game play through visual observation

A Robust Algorithm for Go Image Recognition in Go Game

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