Physical-model-based image processing for feature aided analysis

Smith, Jared; Best, Erin; Sum, E.; Guzel, Yasar; Saville, Michael A.; LoMonte, Lorenzo; Wicks, Michael C.

doi:10.1109/iceaa.2015.7297179

Cited by 3 publications

References 10 publications

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The using of data augmentation in machine learning in image processing tasks in the face of data scarcity

Andriyanov

2020

J. Phys.: Conf. Ser.

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The article presents the results of a study of the efficiency of various neural networks in the limited conditions of the source data and with a number of simple augmentations. In this case, the dependences were obtained for a serial neural network with back propagation of error. For data augmentation, the simplest transformations were used, including the letters tilting (italics), changing the color of letters (from black to red), as well as distortion of the reference images with white Gaussian noise at a signal-to-noise ratio q from 1 to 10. It is shown that the best results of recognition of letters of the Russian alphabet are provided by a network for which all the augmentation methods discussed in this work were used. A study of the dependence of recognition accuracy on the signal-to-noise ratio in all trained neural networkswas also conducted.

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The using of data augmentation in machine learning in image processing tasks in the face of data scarcity

Andriyanov

2020

J. Phys.: Conf. Ser.

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Research of recognition accuracy of dangerous and safe x-ray baggage images using neural network transfer learning

Andriyanov

Volkov

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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The article considers the use of neural networks to solve the problem of recognizing dangerous and safe objects carried in the luggage of airport passengers. A comparative analysis is performed to define the accuracy achieved on the test sample for different convolutional neural networks. It also explores the influence of various regularizations on the accuracy of a two-class classification. The increased probability of correct recognition is achieved due to augmentation, reset weights and saturation of the network. The method of transfer training is used to increase the efficiency of the recognizer. In this case, a study was carried out for the transfer of various neural networks.

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Improvement of continuous-wave radar measurements in a partially controlled environment

Campbell

Smith

Miller

et al. 2016

2016 IEEE Conference on Antenna Measurements &Amp; Applications (CAMA)

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Physical-model-based image processing for feature aided analysis

Cited by 3 publications

References 10 publications

The using of data augmentation in machine learning in image processing tasks in the face of data scarcity

The using of data augmentation in machine learning in image processing tasks in the face of data scarcity

Research of recognition accuracy of dangerous and safe x-ray baggage images using neural network transfer learning

Improvement of continuous-wave radar measurements in a partially controlled environment

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