PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The important task of 2D image classification and segmentation is the extraction of the local geometrical features. The convolution neural network is the common approach last years in this field. Usually, the neighborhood of each pixel of the image is implemented to collect local geometrical information. The information for each pixel is stored in a matrix. Then, Convolutional Auto-Encoder (CAE) is utilized to extract the main geometrical features. In this paper, we propose a neural network based on CAE to solve the extraction of local geometrical features problem for noisy images. Computer simulation results are provided to illustrate the performance of the proposed method.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Vitaly Kober, Sergei Voronin, Artyom Makovetskii, Dmitrii Zhernov, Aleksei Voronin, "Convolutional auto-encoder to extract local features of 2D images," Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126741J (4 October 2023); https://doi.org/10.1117/12.2677848