Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Segmentation of Polygons with Different Colors and its Application to the Development of Vision-based Tangram Puzzle Game

다른 색으로 구성된 다각형들의 분할과 이를 이용한 영상 인식 기반 칠교 퍼즐 놀이 개발

  • Lee, Jihye (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Yi, Kang (School of Computer Science and Electrical Engineering, Handong Global University) ;
  • Kim, Kyungmi (School of Global Leadership, Handong Global University)
  • Received : 2017.09.04
  • Accepted : 2017.11.29
  • Published : 2017.12.31
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Abstract

Tangram game consists of seven pieces of polygons such as triangle, square, and parallelogram. Typical methods of image processing for object recognition may suffer from the existence of side thickness and shadow of the puzzle pieces that are dependent on the pose of 3D-shaped puzzle pieces and the direction of light sources. In this paper, we propose an image processing method that recognizes simple convex polygon-shaped objects irrespective of thickness and pose of puzzle objects. Our key algorithm to remove the thick side of piece of puzzle objects is based on morphological operations followed by logical operations with edge image and background image. By using the proposed object recognition method, we are able to implement a stable tangram game applications designed for tablet computers with front camera. As the experimental results, recognition rate is about 86 percent and recognition time is about 1ms on average. It shows the proposed algorithm is fast and accurate to recognize tangram blocks.

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References

  1. P. Hung, G. Hwang, Y. Lee, and I. Su, “A Cognitive Component Analysis Approach for Developing Game-based Spatial Learning Tools,” Computers and Education, Vol. 59, No. 2, pp. 762-773, 2012. https://doi.org/10.1016/j.compedu.2012.03.018
  2. S. Pricea, C. Jewitta, and L.C. Lanna, "The Role of iPads in Pre-school Children's Mark Making Development," Computers and Education, Vol. 87, pp. 131-141, 2015. https://doi.org/10.1016/j.compedu.2015.04.003
  3. S. Aronin and K. Floyd, “Using an iPad in Inclusive Preschool Classrooms to Introduce STEM Concepts,” Teaching Exceptional Children, Vol. 45, No. 4, pp. 34-39, 2013.
  4. H.E. Mcewing, “Play to Learn,” Children Libraries, Vol. 10, No. 3, pp. 45-51, 2012.
  5. K.K. Park and K. Yi, "Real-time Object Recognition for Children Education Applications Based on Augmented Reality," Journal of Korea Multimedia Society, Vol. 20, No. 1, pp. 17-31, 2017. https://doi.org/10.9717/kmms.2017.20.1.017
  6. M. Tchoshanov, "Building Students' Mathematical Proficiency: Connecting Mathematical Ideas Using the Tangram," Learning and Teaching Mathematics, Vol. 10, pp. 16-23, 2011.
  7. N.M. Siew, C.L. Chong, and M.R. Abdullah, "Facilitating Students' Geometric Thinking Through Van Hiele's Phase-based Learning Using Tangram," Journal of Social Sciences, Vol. 9, No. 3, pp. 101-111, 2013. https://doi.org/10.3844/jssp.2013.101.111
  8. M.M. Neumann, "Young Children's Use of Touch Screen Tablets for Writing and Reading at Home: Relationships with Emergent Literacy," Computers and Education, Vol. 97, pp. 61-68, 2016. https://doi.org/10.1016/j.compedu.2016.02.013
  9. D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  10. H. Bay, T. Tuytelaars, and V.L. Gool, "SURF: Speeded Up Robust Features," Proceeding of European Conference on Computer Vision, Vol. 3951, pp. 404-417, 2006.
  11. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An Efficient Alternative to SIFT or SURF," Proceeding of International Conference on Computer Vision, pp. 2564-2571, 2011.
  12. D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, "Scalable Object Detection Using Deep Neural Networks," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147-2154, 2014.
  13. F. Pedro and H. Daniel, Distance Transforms of Sampled Functions, Technical Report TR 2004-1963, Computer Science Department, Cornell University, 2004.