Abstract
Given an affine transform image having a distorted appearance, if a transform function is known, then an inverse transform function can be applied to the image to produce the undistorted original image. However, if the transform function is not known, can we estimate its values by searching through this large affine transform space? Here, an unknown affine transform function of a given digit is estimated by searching through the affine transform space using the Particle Swarm Optimization (PSO) approach. In this paper, we present important concepts of the proposed approach, describe the experimental design and discuss our results which favorably support the potential of the approach. We successfully demonstrate the potential of this novel approach that could be used to classify a large set of unseen distorted affine transform digits with only a small set of digit prototypes.
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Notes
- 1.
In contrast to the pixel appearance-based approach, other approaches may possess 3D information of an object of interest.
- 2.
Lecun’s state of the art LeNet-5 can be viewed online at http://yann.lecun.com/exdb/lenet/index.html.
- 3.
Available from http://yann.lecun.com/exdb/mnist/.
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Acknowledgments
We wish to thank anonymous reviewers for their comments that have helped improve this paper. We would like to thank the GSR office for their partial financial support given to this research.
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Phon-Amnuaisuk, S., Lee, SY. (2016). Classification of Distorted Handwritten Digits by Swarming an Affine Transform Space. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_19
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DOI: https://doi.org/10.1007/978-3-319-41009-8_19
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