Abstract
Abstract. Automatic detection and extraction of corresponding features is very crucial in the development of an automatic image registration algorithm. Different feature descriptors have been developed and implemented in image registration and other disciplines. These descriptors affect the speed of feature extraction and the measure of extracted conjugate features, which affects the processing speed and overall accuracy of the registration scheme. This article is aimed at reviewing the performance of most-widely implemented feature descriptors in an automatic image registration scheme. Ten (10) descriptors were selected and analysed under seven (7) conditions viz: Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. The analysis shows that though four (4) descriptors performed better than the other Six (6), no single feature descriptor can be affirmed to be the best, as different descriptors perform differently under different conditions. The Modified Harris and Stephen Corner Detector (MHCD) proved to be invariant to scale and zoom while it is excellent in robustness, repeatability, localization and efficiency, but it is variant to rotation. Also, the Scale Invariant feature Transform (SIFT), Speeded Up Robust Features (SURF) and the Maximally Stable Extremal Region (MSER) algorithms proved to be invariant to scale, zoom and rotation, and very good in terms of repeatability, localization and efficiency, though MSER proved to be not as robust as SIFT and SURF. The implication of the findings of this research is that the choice of feature descriptors must be informed by the imaging conditions of the image registration analysts.
Highlights
This article is aimed at providing an empirical review of the strength and weaknesses of the most implemented feature descriptors as used in automatic registration of overlapping images
The analysis shows that only Modified Harris and Stephens Corner Detector (MHCD) is excellent in terms of robustness, repeatability, efficiency, and localization
1.3.2 Scale invariant feature transform algorithm (SIFT): The Scale Invariant Feature Transform (SIFT) descriptor is a vector of 128 values, each between [0 - 1]
Summary
This article is aimed at providing an empirical review of the strength and weaknesses of the most implemented feature descriptors as used in automatic registration of overlapping images. The analysed descriptors are the Scale Invariant Feature Transform (SIFT), the Speeded Up Robust Features (SURF), Modified Harris and Stephens Corner Detector (MHCD), the Maximally Stable Extremal Regions (MSER), and the Features from Accelerated Segment Test (FAST). The review first provided a broad overview of feature detection and extraction, before providing a summary of the characteristics of some of the feature descriptors. It further analysed the qualities of the selected descriptors under seven (7) conditions which are Invariance to rotation, scale and zoom, their robustness, repeatability, localization and efficiency using UAV acquired images. Details of the procedures of implementing the three descriptors adjudged to outperform others were provided and experimental findings of the performance evaluation were presented
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