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ON APPLICATION OF HEAT EQUATION IN DEEP LEANING RECOGNITION OF GOOGLE STREET VIEW NUMBERS
Abstract
Recognition of the numbers is a necessary condition for the development of 3D street viewers. Using information about the position of the viewer relative to the detected site by self-driving car will make it possible to adjust the path and increase the accuracy of its implementation. The unmanned device (unmanned aerial vehicle or self-driving car) is equipped with a video camera or camcorder system. In the process of detecting house numbers, the image from the camera contains noises and redundant information. The preceding recognition step involves preliminary processing of the image obtained from the optical system. One of the techniques for removing noise from an incoming image is to perform an anti-aliasing operation. In this work we consider the filter presented by Peron and Malik, which is a modification of the heat equation. The choice of the method is due to the presence of a boundary detector, which allows us to save significant edges on the image when filtering noise. This is necessary, for example, for algorithms of recognition based on contour analysis. The recognition of filtered image is implemented in R with help of keras package. As a training set we use the Street View House Numbers (SVHN) Dataset, which is a real-world image dataset with minimal requirement on data preprocessing and formatting. It was shown that the primary filtering of test images allows us to increase accuracy of number recognition.
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References11
V. Martsenyuk, Z. Mayhruk, M. Karpinski, N. Milian, I. Andrushchak and O. Veselska, On implementation of decision tree induction in cloud platforms, 2019 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 2019, pp. 1-6. DOI: 10.1109/ICASET.2019.8714450;
Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, Tsuhan Chen, Recent advances in convolutional neural networks, Pattern Recognition, vol. 77, pp 354- 377, 2018, DOI: 10.1016/j.patcog.2017.10.013;
Hafiz Tayyab Mustafa, Jie Yang, Masoumeh Zareapoor, Multi-scale convolutional neural network for multi-focus image fusion, Image and Vision Computing, vol. 85, pp 26-35, 2019, DOI: 10.1016/j.imavis.2019.03.001;
Raymond Ptucha, Felipe Petroski Such, Suhas Pillai, Frank Brockler, Vatsala Singh, Paul Hutkowski, Intelligent character recognition using fully convolutional neural networks, Pattern Recognition, vol. 88, pp 604-613, 2019, DOI: 10.1016/j.patcog.2018.12.017;
Boukaye Boubacar Traore, Bernard Kamsu-Foguem, Fana Tangara, Deep convolution neural network for image recognition, Ecological Informatics, vol. 48, pp 257-268, 2018, DOI: 10.1016/j.ecoinf.2018.10.002;
Perona, Pietro, Jitendra Malik, Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, vol. 12/issue 7, pp 629-639, 1990);
Buades Antoni, Bartomeu Coll, Jean-Michel Morel, Neighborhood filters and PDE’s, Numerische Mathematik, vol. 105/issue 1, pp 1-34, 2006;
Bartosz Jablonski, Ryszard Klempous, Marek Kulbacki, PDE-based filtering of motion sequences, Journal of Computational and Applied Mathematics, vol. 189/issues 1–2, pp 660-675, 2006, DOI: 10.1016/j.cam.2005.03.033;
The Street View House Numbers (SVHN) Dataset http://ufldl.stanford.edu/housenumbers/;
Géron, Aurélien. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O'Reilly Media, Inc., 2017;
Gulli, Antonio, and Sujit Pal. Deep Learning with Keras. Packt Publishing Ltd, 2017.
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