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Proceedings Paper

Wavelet based edge feature enhancement for convolutional neural networks
Author(s): D. D. N. De Silva; S. Fernando; I. T. S. Piyatilake; A. V. S. Karunarathne
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Paper Abstract

Convolutional neural networks are able to perform a hierarchical learning process starting with local features. However, a limited attention is paid to enhancing such elementary level features like edges. We propose and evaluate two wavelet-based edge-feature enhancement methods to preprocess the input images to convolutional neural networks. The first method develops representations by decomposing the input images using wavelet transform and limited reconstructing subsequently. The second method develops such feature-enhanced inputs to the network using local modulus maxima of wavelet coefficients. For each method, we have developed a new preprocessing layer by implementing each proposed method and have appended to the network architecture. Our empirical evaluations demonstrate that the proposed methods are outperforming the baselines and previously published work.

Paper Details

Date Published: 15 March 2019
PDF: 10 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110412R (15 March 2019); doi: 10.1117/12.2522849
Show Author Affiliations
D. D. N. De Silva, Univ. of Moratuwa (Sri Lanka)
S. Fernando, Univ. of Moratuwa (Sri Lanka)
I. T. S. Piyatilake, Univ. of Moratuwa (Sri Lanka)
A. V. S. Karunarathne, Univ. of Moratuwa (Sri Lanka)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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