Deep Morphological Neural Networks

Yucong Shen, Frank Y. Shih, Xin Zhong, I. Cheng Chang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number2252023
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number12
StatePublished - Sep 30 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • Deep learning
  • mathematical morphology
  • morphological layer
  • morphological neural networks


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