In order to solve the internal feature matching problem of nonlinear flexible biological tissues in MR images, This paper proposes a feature point descriptor generation model based on transfer learning and convolutional neural networks TBNet . Firstly, the Siamese network structure model is combined with transfer learning to obtain a pre-trained CNN model and then this paper proposes a batch-by-batch model fine-tuning strategy. Secondly, the extracted feature point descriptor is obtained using the fine-tuned model. Finally, Experiments show that the TBNet has higher robustness and accuracy than traditional SIFT, SURF and the state-of-the-art VGG16-based models.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.