NDLI: Image alignment by parameter hypersurface learning

Content Provider	IET Digital Library
Author	Choi, H. C. Ahn, B. H.
Abstract	A new concept of image alignment method is introduced based on machine learning technique. The proposed method estimates image alignment parameters by using a regression hypersurface, which maps image feature space onto alignment parameter space. The regression hypersurface is obtained by learning an appropriate type of neural network from a large number of training samples, which consist of image features and corresponding alignment parameters. Compared with the previous method that utilises a regression hyperplane, this method achieved a significant improvement in convergence range to apply for alignment of rectangular region in long term image sequences in low frame rate.
Starting Page	1526
Ending Page	1528
Page Count	3
ISSN	00135194
Volume Number	52
e-ISSN	1350911X
Issue Number	Issue 18, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/52/18
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.1576
Journal	Electronics Letters
Publisher Date	2016-08-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Image Alignment Method Image Feature Image Sequence Knowledge Engineering Technique Learning in AI Long Term Image Sequence Machine Learning Technique Neural Network Optical, Image And Video Signal Processing Parameter Hypersurface Learning Regression Analysis Regression Hypersurface Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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