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Image Stitching in Dynamic Scene for Computer Vision Project-Base Learning

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Computer Science and Education (ICCSE 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1813))

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Abstract

In this paper, we present a novel computer vision course project. The project aims to stitch images captured in the dynamic scene, which is challenging since traditional methods rely on the assumption of static scenes. However, it provides the opportunity to solve the frontier computer vision problem by using the theories and skills taught in the lectures. The proposed project has three milestones that cover most of the content in the course syllabus and require the students to use interdisciplinary CV techniques, including traditional and deep-learning-based ones. We provide a detailed description of the proposed course project, including the goal, milestones, schedule, and evaluation strategy. We believe it can serve as a practical CV education resource for other higher educators by applying to their CV-related courses.

This work was supported by a grant from NSFC (No. 62102145 and 62002107) and Jiangxi Provincial 03 Special Foundation and 5G Program (No. 20224ABC03A05).

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References

  1. Advances in computer vision. http://6.869.csail.mit.edu/sp22/schedule.html

  2. Cornell University: CS6670 - computer vision. https://www.cs.cornell.edu/courses/cs6670/2021fa/

  3. CS231A: Computer vision, from 3D reconstruction to recognition. https://web.stanford.edu/class/cs231a/project.html

  4. CS231N: Deep learning for computer vision. http://cs231n.stanford.edu/project.html

  5. Harvard University: CSCI E-25 computer vision. https://canvas.harvard.edu/courses/96434/assignments/syllabus

  6. Adarsh, P., Rathi, P., Kumar, M.: Yolo v3-tiny: object detection and recognition using one stage improved model. In: International Conference on Advanced Computing and Communication Systems (2020)

    Google Scholar 

  7. Bay, H., Tuytelaars, T., Gool, L.V.: Surf: speeded up robust features. In: ECCV (2006)

    Google Scholar 

  8. Bebis, G., Egbert, D., Shah, M.: Review of computer vision education. IEEE Trans. Educ. 46(1), 2–21 (2003)

    Article  Google Scholar 

  9. Bradski, G., Kaehler, A.: Opencv. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  10. Brown, M., Lowe, D.G., et al.: Recognising panoramas. In: ICCV (2003)

    Google Scholar 

  11. Chen, C.H., Yang, Y.C.: Revisiting the effects of project-based learning on students’ academic achievement: a meta-analysis investigating moderators. Educ. Res. Rev. 26, 71–81 (2019)

    Article  Google Scholar 

  12. DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: self-supervised interest point detection and description. In: CVPR Workshops (2018)

    Google Scholar 

  13. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. In: IJCV (2010)

    Google Scholar 

  14. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  15. Geronimo, D., Serrat, J., Lopez, A.M., Baldrich, R.: Traffic sign recognition for computer vision project-based learning. IEEE Trans. Educ. 56(3), 364–371 (2013)

    Article  Google Scholar 

  16. Guilluy, W., Oudre, L., Beghdadi, A.: Video stabilization: overview, challenges and perspectives. Signal Process. Image Commun. 90, 116015 (2021)

    Article  Google Scholar 

  17. Guo, P., Saab, N., Post, L.S., Admiraal, W.: A review of project-based learning in higher education: student outcomes and measures. Int. J. Educ. Res. 102, 101586 (2020)

    Article  Google Scholar 

  18. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)

    MATH  Google Scholar 

  19. Lao, Y., Yang, J., Wang, X., Lin, J., Cao, Y., Song, S.: Augmenting TV shows via uncalibrated camera small motion tracking in dynamic scene. In: ACM MM (2021)

    Google Scholar 

  20. Le, H., Liu, F., Zhang, S., Agarwala, A.: Deep homography estimation for dynamic scenes. In: CVPR (2020)

    Google Scholar 

  21. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  22. Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV (1999)

    Google Scholar 

  23. Lu, X.X.: A review of solutions for perspective-n-point problem in camera pose estimation. In: Journal of Physics: Conference Series (2018)

    Google Scholar 

  24. Maxwell, B.A.: A survey of computer vision education and text resources. Int. J. Pattern Recognit. Artif. Intell. 15(05), 757–773 (2001)

    Article  Google Scholar 

  25. Orhei, C., Vert, S., Mocofan, M., Vasiu, R.: End-to-end computer vision framework: an open-source platform for research and education. Sensors 21(11), 3691 (2021)

    Article  Google Scholar 

  26. O’Mahony, N., et al.: Deep learning vs. traditional computer vision. In: Arai, K., Kapoor, S. (eds.) CVC 2019. AISC, vol. 943, pp. 128–144. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-17795-9_10

    Chapter  Google Scholar 

  27. Panciroli, C., Rivoltella, P.C., Gabbrielli, M., Richter, O.Z.: Artificial intelligence and education: new research perspectives. Form@ re-Open Journal per la formazione in rete 20(3), 43–67 (2020)

    Google Scholar 

  28. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint (2018)

    Google Scholar 

  29. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: ICCV (2011)

    Google Scholar 

  30. Saputra, M.R.U., Markham, A., Trigoni, N.: Visual slam and structure from motion in dynamic environments: a survey. ACM Comput. Surv. (CSUR) 51(2), 1–36 (2018)

    Article  Google Scholar 

  31. Satılmış, Y., Tufan, F., Şara, M., Karslı, M., Eken, S., Sayar, A.: CNN based traffic sign recognition for mini autonomous vehicles. In: Świątek, J., Borzemski, L., Wilimowska, Z. (eds.) ISAT 2018. AISC, vol. 853, pp. 85–94. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99996-8_8

    Chapter  Google Scholar 

  32. Seničić, M., Matijević, M., Nikitović, M.: Teaching the methods of object detection by robot vision. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0558–0563. IEEE (2018)

    Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Yizhen Lao, Yu Cao, Huiqing Zhang & Yufeng Zhang

Authors
  1. Yizhen Lao
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  2. Yu Cao
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  3. Huiqing Zhang
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  4. Yufeng Zhang
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Corresponding author

Correspondence to Yufeng Zhang .

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Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Wenxing Hong

  2. Sichuan University, Chengdu, China

    Yang Weng

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Lao, Y., Cao, Y., Zhang, H., Zhang, Y. (2023). Image Stitching in Dynamic Scene for Computer Vision Project-Base Learning. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1813. Springer, Singapore. https://doi.org/10.1007/978-981-99-2449-3_33

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  • DOI: https://doi.org/10.1007/978-981-99-2449-3_33

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  • Print ISBN: 978-981-99-2448-6

  • Online ISBN: 978-981-99-2449-3

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