<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, the image Gabor features extracted by Gabor wavelet are fused with the image grayscale map to construct the enhanced Gabor features, and then combined with the characteristics of Gabor wavelet and convolutional layer, the Gabor feature extraction module, parallel convolution module and spatial transformation pooling module are designed. The corresponding Gabor convolutional layer and Gabor convolutional neural network are constructed using the appropriate module in accordance with the image recognition task application scenario. The convex set projection image super-resolution reconstruction method is used in this paper to improve the resolution of images with low resolution. The construction of a computerized image recognition system involves combining a Gabor convolutional neural network and a convex set projection method. This system has been tested and found to have a recognition accuracy of 93.5% for object images. This system’s ability to accurately recognize low-resolution shadow-obscured face images is possible thanks to using the convex set projection method to reconstruct the image and recognize it accurately with an accuracy of up to 93.85%. This system’s recognition performance for complex images has been proven through experiments.</p>
</abstract>

In this paper, the image Gabor features extracted by Gabor wavelet are fused with the image grayscale map to construct the enhanced Gabor features, and then combined with the characteristics of Gabor wavelet and convolutional layer, the Gabor feature extraction module, parallel convolution module and spatial transformation pooling module are designed. The corresponding Gabor convolutional layer and Gabor convolutional neural network are constructed using the appropriate module in accordance with the image recognition task application scenario. The convex set projection image super-resolution reconstruction method is used in this paper to improve the resolution of images with low resolution. The construction of a computerized image recognition system involves combining a Gabor convolutional neural network and a convex set projection method. This system has been tested and found to have a recognition accuracy of 93.5% for object images. This system’s ability to accurately recognize low-resolution shadow-obscured face images is possible thanks to using the convex set projection method to reconstruct the image and recognize it accurately with an accuracy of up to 93.85%. This system’s recognition performance for complex images has been proven through experiments.

Research and realization of computer image recognition system based on digital projection technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the image Gabor features extracted by Gabor wavelet are fused with the image grayscale map to construct the enhanced Gabor features, and then...

Research and realization of computer image recognition system based on digital projection technology

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0111

Mots clés
Convolutional neural network, Convex set projection method, Gabor wavelet, Image recognition system, Super-resolution reconstruction

© 2024 Qian Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research and realization of computer image recognition system based on digital projection technology

Publié en ligne: 31 janv. 2024

Pages: -

Reçu: 15 déc. 2023

Accepté: 24 déc. 2023

DOI: https://doi.org/10.2478/amns-2024-0111

Mots clésConvolutional neural network, Convex set projection method, Gabor wavelet, Image recognition system, Super-resolution reconstruction

© 2024 Qian Ma, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Convolutional neural network, Convex set projection method, Gabor wavelet, Image recognition system, Super-resolution reconstruction