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Employing a Restricted Set of Qualitative Relations in Recognizing Plain Sketches

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KI 2017: Advances in Artificial Intelligence (KI 2017)

Abstract

In this paper, we employ aspects of machine learning, computer vision, and qualitative representations to build a classifier of plain sketches. The paper proposes a hybrid technique for accurately recognizing hand-drawn sketches, by relying on a set of qualitative relations between the strokes that compose such sketches, and by taking advantage of two major perspectives for processing images. Our implementation shows promising results for recognizing sketches that have been hand-drawn by human participants.

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Notes

  1. 1.

    Think of the 9 relations filling a \(3\times 3\) tic-tac-toe-like board:

    figure a

    .

  2. 2.

    Note that the labeling of the strokes is intended to reflect the successiveness of the strokes; e.g., \(s_{4}\) is sketched after \(s_{3}\). This is vital, especially because the presented positional relations are not commutative (except \(\equiv \), of course).

  3. 3.

    This is one of the predefined functions within the Matlab computer vision toolbox.

References

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Acknowledgements

The authors acknowledge the role of the Artificial Intelligence and Cognitive Science Lab that has recently been established at the Faculty of Science, Ain Shams University, Cairo, Egypt, supported by the Institute of Cognitive Science at the University of Osnabrück in Germany. The work in this paper is part of the activities of the JESICS project, which has been funded by the German Academic Exchange Service (DAAD) within the framework of the German-Arab Research partnerships Programme Line 4 under grant agreement 57247603.

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Correspondence to Ahmed M. H. Abdelfattah .

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Abdelfattah, A.M.H., Zakaria, W. (2017). Employing a Restricted Set of Qualitative Relations in Recognizing Plain Sketches. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-67190-1_1

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