Using Local, Contextual, and Deep Convolutional Neural Network Features in Image Registration
Conference object
Accepted version
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https://hdl.handle.net/10642/9436Utgivelsesdato
2020-06Metadata
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Originalversjon
Shrestha R: Using Local, Contextual, and Deep Convolutional Neural Network Features in Image Registration. In: ACM NY. ICCMS '20: Proceedings of the 12th International Conference on Computer Modeling and Simulation, 2020. Association for Computing Machinery (ACM) p. 56-60 https://doi.org/10.1145/3408066.3408098Sammendrag
Image registration is a well-known problem that arises
in many applications in the fields of computer vision,
remote sensing, and medical imaging. Many registration
methods have been proposed in the literature. However,
no single method works well in all kinds of images. In
this work, local features and context-based augmented
features are used in order to improve the accuracy of the
image registration. Furthermore, an attempt has been
made to use deep convolutional neural network features
on top of those features for further improvement. The
paper presents comparative results on image registration
with and without feature augmentation and the deep
convolutional neural network features. The results from
the methods on a widely used benchmark dataset from
the University of Oxford confirm improvement in the
accuracy of image registration when local and augmented
features are used.
Utgiver
Association for Computing Machinery (ACM)Serie
International Conference on Computer Modeling and Simulation;ICCMS '20: Proceedings of the 12th International Conference on Computer Modeling and SimulationBeslektede innførsler
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