Fingerprint Classification Based on Sparse Representation Using Rotation-Invariant Features

2012 ◽  
pp. 631-641
Author(s):  
Yong A ◽  
Tiande Guo ◽  
Xiao Liu ◽  
Guangqi Shao
Keyword(s):  
Sparse Representation ◽  
Rotation Invariant ◽  
Fingerprint Classification ◽  
Invariant Features

scholarly journals Analysis of the Repeatability of SIFT and SURF Descriptors Techniques for Underwater Image Preprocessing

2021 ◽  
pp. 24-31
Author(s):  
Shubhangi N. Ghate ◽  
Dr. Mangesh Nikose
Keyword(s):  
Prior Knowledge ◽  
State Of The Art ◽  
Rotation Invariant ◽  
Image Preprocessing ◽  
Retrieval Time ◽  
Retrieval Accuracy ◽  
Invariant Features ◽  
Underwater Image ◽  
Geometrical Constraints ◽  
Time Required

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.

scholarly journals Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images

2008 ◽  
Vol 2008 (1) ◽  
Author(s):  
Javier A. Montoya-Zegarra ◽  
João Paulo Papa ◽  
Neucimar J. Leite ◽  
Ricardo da Silva Torres ◽  
Alexandre Falcão
Keyword(s):  
Rotation Invariant ◽  
Scale Invariant ◽  
Invariant Features
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