Descriptor Matching for a Discrete Spherical Image With a Convolutional Neural Network

In this paper, we propose a method of extracting feature descriptors from discrete spherical images using convolutional neural networks (CNNs). First, a captured full-view image is mapped to a discrete spherical image. Second, the features-from-accelerated-segment test algorithm is used to extract f...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.20748-20755
Main Authors: Shan, Yuhao, Li, Shigang
Format: Article
Language:English
Subjects:
Accelerated tests
Algorithms
Artificial neural networks
Cameras
Convolutional neural network
Convolutional neural networks
Data structures
Detectors
discrete spherical image
feature descriptor
Feature extraction
full-view image
Neural networks
Noise levels
Robustness
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Summary:In this paper, we propose a method of extracting feature descriptors from discrete spherical images using convolutional neural networks (CNNs). First, a captured full-view image is mapped to a discrete spherical image. Second, the features-from-accelerated-segment test algorithm is used to extract feature points in the discrete spherical image. Finally, an unsupervised CNN is used to obtain the descriptors of patches around each feature point. In the experiments, we compare these descriptors' performance to the closest existing state-of-the-art feature descriptors of discrete spherical images, spherical oriented FAST and rotated BRIEF (SPHORB), for image pairs having different camera rotation, noise levels, and general motions. The experimental results demonstrate that our proposed CNN-based discrete spherical image feature descriptors clearly outperform SPHORB both in accuracy and robustness.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2825477