Abstract
In this chapter, the binarization of matrix codes is investigated as an application of supervised learning of image processing tasks using a recurrent version of the Neural Abstraction Pyramid.
The desired network output is computed using an adaptive thresholding method for images of high contrast. The network is trained to iteratively produce it even when the contrast is lowered and typical noise is added to the input.
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© 2003 Springer-Verlag Berlin Heidelberg
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Behnke, S. (2003). Binarization of Matrix Codes. In: Hierarchical Neural Networks for Image Interpretation. Lecture Notes in Computer Science, vol 2766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45169-3_8
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DOI: https://doi.org/10.1007/978-3-540-45169-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40722-5
Online ISBN: 978-3-540-45169-3
eBook Packages: Springer Book Archive