LindaWu

Conclusion

This project presents comparison of ORB, BRISK, SURF, FAST, Harris and MSER feature-detector-descriptors. SURF and ORB are found to be the most scale invariant feature detectors (on the basis of inlier percentage) that have survived wide-spread scale variations. BRISK is found to be least scale invariant (FAST and Harris are not scale invariant). SURF and ORB are also more rotation invariant than others. FAST and Harris have higher accuracy for image rotations as compared to the rest. Although, ORB, BRISK are the most efficient algorithms that can detect a huge amount of features, the matching time for such a large number of features prolongs the total image matching time. On the contrary, FAST and SURF perform fastest image matching but their accuracy gets compromised.

The quantitative comparison (Appendix E) has shown that the generic order of feature-detector-descriptors for their ability to detect high quantity of features (Inliers Percentage) is:

SURF>Harris>ORB>BRISK>FAST>MSER

● The sequence of algorithms for computational efficiency of feature-detection-description per feature-point is:

ORB>SURF>Harris>FAST>BRISK>MSER

● The order of efficient feature-matching per feature-point is:

Harris>SURF>BRISK>FAST>MSER>ORB

ORB is most efficient feature-detection-description algorithm, while it is most inefficient during feature matching.

● The feature-detector-descriptors can be rated for the speed of total image matching as:

ORB>FAST>SURF>Harris>MSER>BRISK

● The image matching accuracy of descriptors can be rated as:

FAST>Harris>BRISK>MSER>ORB>SURF

The overall accuracy of BRISK and MSER are found to be highest for all types of geometric transformations (as FAST and Harris are not scale invariant), and ORB performs the best with regards to speed versus accuracy.

Reference

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[3] Image Alignment (Feature Based) using OpenCV (C++/Python) https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/

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