"Convolutional Neural Network Learning Versus Traditional Segmentation for ..."

Ruxandra Stoean et al. (2019)

> Home

Details and statistics

DOI: 10.1007/978-3-030-20521-8_71

access: closed

type: Conference or Workshop Paper

metadata version: 2023-03-21

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/iwann/StoeanSSJ19
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/iwann/StoeanSSJ19
Ruxandra Stoean, Catalin Stoean, Adriana Samide, Gonzalo Joya:
Convolutional Neural Network Learning Versus Traditional Segmentation for the Approximation of the Degree of Defective Surface in Titanium for Implantable Medical Devices. IWANN (1) 2019: 871-882

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.