This repository contains the implementation details of our paper:

"TRADITIONAL METHOD INSPIRED DEEP NEURAL NETWORK FOR EDGE DETECTION"
by Jan Kristanto Wibisono , Hsueh-Ming Hang

• Python 3.7
• Pytorch 1.4

Our systems contain three basic modules: Feature Extractor, Enrichment, and Summarizer, which roughly correspond to gradient, low pass filter, and pixel connection in the traditional edge detection schemes.

Comparison of complexity and accuracy performance among various edge detection schemes. Our proposed methods (Green). BDCN family (Red). Other methods (Blue). ODS (Transparent label). Number of Parameter (Orange label)

    python inference.py

@inproceedings{tin2020,
  title={Traditional Method Inspired Deep Neural Network for Edge Detection},
  author={Jan Kristanto Wibisono and Hsueh-Ming Hang},
  booktitle={IEEE International Conference on Image Processing (ICIP)},
  pages={soon--soon},
  year={2020}
}