COMPARISON OF IMAGE FEATURE DETECTORS AND EVALUATION OF THEIR STATISTICAL CHARACTERISTICS

2021 ◽  
2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.


2016 ◽  
Vol 25 (1) ◽  
pp. 010501 ◽  
Author(s):  
Bruno Ferrarini ◽  
Shoaib Ehsan ◽  
Naveed Ur Rehman ◽  
Klaus D. McDonald-Maier

2015 ◽  
Vol 2 (2) ◽  
pp. 45-50 ◽  
Author(s):  
Taihú Pire ◽  
Thomas Fischer ◽  
Jan Faigl

This work evaluates an impact of image feature extractors on the performance of a visual SLAM method in terms of pose accuracy and computational requirements. In particular, the S-PTAM (Stereo Parallel Tracking and Mapping) method is considered as the visual SLAM framework for which both the feature detector and feature descriptor are parametrized. The evaluation was performed with a standard dataset with ground-truth information and six feature detectors and four descriptors. The presented results indicate that the combination of the GFTT detector and the BRIEF descriptor provides the best trade-off between the localization precision and computational requirements among the evaluated combinations of the detectors and descriptors.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 8564-8573 ◽  
Author(s):  
Bruno Ferrarini ◽  
Shoaib Ehsan ◽  
Ales Leonardis ◽  
Naveed Ur Rehman ◽  
Klaus D. McDonald-Maier

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