Deep Morphological Neural Networks
Document Type
Article
Publication Date
9-30-2022
Abstract
Mathematical morphology intends to extract object features such as geometric and topological structures in digital images. Given a set of target images and original images, it is cumbersome and time-consuming to determine the suitable morphological operations and structuring elements. In this paper, we propose deep morphological neural networks, which include a nonlinear feature extraction layer to learn the structuring element correctly and an adaptive layer to select appropriate morphological operations automatically. We demonstrate the applications of object recognition, including hand-written digits, geometric shapes, traffic signs, and brain tumor. Experimental results show the higher computational efficiency and higher accuracy of our developed model as compared against existing convolutional neural network models.
Identifier
85137401689 (Scopus)
Publication Title
International Journal of Pattern Recognition and Artificial Intelligence
External Full Text Location
https://doi.org/10.1142/S0218001422520231
ISSN
02180014
Issue
12
Volume
36
Recommended Citation
Shen, Yucong; Shih, Frank Y.; Zhong, Xin; and Chang, I. Cheng, "Deep Morphological Neural Networks" (2022). Faculty Publications. 2644.
https://digitalcommons.njit.edu/fac_pubs/2644
