Open AccessAnalysis of the Repeatability of SIFT and SURF Descriptors Techniques for Underwater Image Preprocessing
Author(s) -
Shubhangi N. Ghate,
Mangesh Nikose
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-v4-i3-004
Subject(s) - scale invariant feature transform , artificial intelligence , preprocessor , computer science , computer vision , rotation (mathematics) , pattern recognition (psychology) , point (geometry) , image retrieval , scale (ratio) , feature extraction , image (mathematics) , mathematics , geography , geometry , cartography
To improve the repeatability of SIFT and SURF descriptors, we conducted research to find two methods: first, a method for pre-processing underwater images that does not require prior knowledge of the scene, and second, a method for computing distances that is less expensive in terms of execution time for finding corresponding points. SIFTs (Scale and Rotation Invariant Features) are new features that have been developed. SIFTs (Scale and Rotation Invariant Features) are newly developed features that are based on geometrical constraints between pairs of nearby points around a key point. SIFT is contrasted with cutting-edge local features. SIFT outperforms the state-of-the-art in terms of retrieval time and retrieval accuracy. We have discussed the time required to extract key point features of SIFT and SURF Descriptor.