Date of Award

Spring 2019

Document Type

Thesis

Degree Name

Master of Science in Computer Science - (M.S.)

Department

Computer Science

First Advisor

Shih, Frank Y.

Second Advisor

Wei, Zhi

Third Advisor

Phan, Hai Nhat

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.

Share

COinS