Abstract
We address the problem of shape detection in settings where large shape deformations and occlusions occur with clutter noise present. We propose a quantum model for shapes by applying the quantum path integral formulation to an existing energy model for shapes (a Bayesian-derived cost function). We show that the classical statistical method derived from the quantum method, via the Wick rotation technique, is a voting scheme similar to the Hough transform. The quantum phenomenon of interference drives the quantum method for shape detection to excel, compared to the corresponding classical statistical method or the statistical Bayesian (energy optimization) method. To empirically demonstrate our approach, we focus on simple shapes: circles and ellipses.
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Acknowledgments
The first author thanks the National Science Foundation for the Award Number 1422021, which partially supported this research. Both authors wish to thank Dan Pinkel for numerous interesting conversations about these ideas and methods and the anonymous reviewers for valued feedback.
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Geiger, D., Kedem, Z.M. (2018). Quantum Interference and Shape Detection. In: Pelillo, M., Hancock, E. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2017. Lecture Notes in Computer Science(), vol 10746. Springer, Cham. https://doi.org/10.1007/978-3-319-78199-0_2
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DOI: https://doi.org/10.1007/978-3-319-78199-0_2
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