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- W3015269713 abstract "The nonlinear structure of the artificial neural network is efficient for the classification; however, the choice of features is a fundamental problem due to their direct impact on the network convergence and performance. In this paper, we present a new method of image classification method based on Legendre–Fourier moments using an artificial neural network. We used LFMs to extract features from images. In result, every image is represented by a descriptor vector; these vectors are inputs of our neural network. We tested this model on Fashion-MNIST database and we got important results; the model’s accuracy exceeds 97%. The validity of this proposed method has provided under different transformations." @default.
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- W3015269713 date "2020-01-01" @default.
- W3015269713 modified "2023-09-23" @default.
- W3015269713 title "Image Classification Using Legendre–Fourier Moments and Artificial Neural Network" @default.
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- W3015269713 doi "https://doi.org/10.1007/978-981-15-0947-6_29" @default.
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