On the recognition of weakly blurred, highly contrasting objects by neural networks

Dina Tuliabaeva; Dmitrii Tumakov; Leonid Elshin

doi:10.1117/12.3024891

20 February 2024 On the recognition of weakly blurred, highly contrasting objects by neural networks

Dina Tuliabaeva, Dmitrii Tumakov, Leonid Elshin

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 1306507 (2024) https://doi.org/10.1117/12.3024891
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

The influence of the degree of blur on the recognition of high-contrast simple objects is studied using images of handwritten digits and letters as an example. High-contrast objects are objects that are significantly brighter than the surrounding background. White images of handwritten digits (MNIST) and Latin letters (EMNIST) against a black background measuring 28 by 28 pixels are selected as training and testing data sets. It is found that recognition accuracy decreases linearly as the blur level increases. The work also shows that both an increase of blur and a decrease of blur from the blur level of the training sample worsens recognition. It is concluded that recognition when blur is reduced is worse than when blur is increased. Histograms are presented that demonstrate the dependence of recognition accuracy on the degree of blur. It is shown that the initial weights affect the recognition accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dina Tuliabaeva, Dmitrii Tumakov, and Leonid Elshin "On the recognition of weakly blurred, highly contrasting objects by neural networks", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 1306507 (20 February 2024); https://doi.org/10.1117/12.3024891

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KEYWORDS

Education and training

Matrices

Neural networks

Convolution

Brain mapping

Histograms

Object recognition

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