2D Image Features Detector And Descriptor Selection Expert System
This is an incremental improvement for industrial object recognition, focusing on a specific domain case study.
The paper tackled object recognition of industrial parts by proposing a hierarchical classification method that selects 2D image feature detectors and descriptors, achieving better performance than using single methods like ORB, SIFT, or FREAK, though at a slower speed.
Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.