The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Deep morphological neural networks
Author:	Shen, Yucong
View Online:	njit-etd2019-017 (x, 32 pages ~ 1.1 MB pdf)
Department:	Department of Computer Science
Degree:	Master of Science
Program:	Computer Science
Document Type:	Thesis
Advisory Committee:	Shih, Frank Y. (Committee chair) Wei, Zhi (Committee member) Phan, Hai Nhat (Committee member)
Date:	2019-05
Keywords:	Deep learning Deep morphological neural network Morphological layer Image morphology
Availability:	Unrestricted
Abstract:	Mathematical morphology is a theory and technique applied to collect features like geometric and topological structures in digital images. Determining suitable morphological operations and structuring elements for a give purpose is a cumbersome and time-consuming task. In this paper, morphological neural networks are proposed to address this problem. Serving as a non-linear feature extracting layers in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring element correctly, and an adaptive layer can automatically select appropriate morphological operations. For high level applications, the proposed morphological neural networks are tested on several classification datasets which are related to shape or geometric image features, and the experimental results have confirmed the tradeoff between high computational efficiency and high accuracy.