Development of Deep Learning Framework for Mathematical Morphology

Frank Y. Shih, Yucong Shen, Xin Zhong

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Mathematical morphology has been applied as a collection of nonlinear operations related to object features in images. In this paper, we present morphological layers in deep learning framework, namely MorphNet, to perform atomic morphological operations, such as dilation and erosion. For propagation of losses through the proposed deep learning framework, we approximate the dilation and erosion operations by differential and smooth multivariable functions of the softmax function, and therefore enable the neural network to be optimized. The proposed operations are analyzed by the derivative of approximation functions in the deep learning framework. Experimental results show that the output structuring element of a morphological neuron and the target structuring element are matched to confirm the efficiency and correctness of the proposed framework.

Original languageEnglish (US)
Article number1954024
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume33
Issue number6
DOIs
StatePublished - Jun 15 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Mathematical morphology
  • autoencoder
  • convolutional network
  • deep learning

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