Robust and Invariant Phase Based Local Feature Matching

2014 ◽  
Author(s):  
Anders Hast
Keyword(s):  
Feature Matching ◽  
Local Feature

scholarly journals Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

2020 ◽  
Vol 10 (7) ◽  
pp. 2474
Author(s):  
Honglie Wang ◽  
Shouqian Sun ◽  
Lunan Zhou ◽  
Lilin Guo ◽  
Xin Min ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Feature Matching ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
The State ◽  
Local Feature ◽  
Public Security ◽  
Matching Model ◽  
Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

FEATURE GROUP MATCHING: A NOVEL METHOD TO FILTER OUT INCORRECT LOCAL FEATURE MATCHINGS

2014 ◽  
Vol 28 (05) ◽  
pp. 1450012 ◽  
Author(s):  
EMANUELE FRONTONI ◽  
ADRIANO MANCINI ◽  
PRIMO ZINGARETTI
Keyword(s):  
Image Retrieval ◽  
Feature Matching ◽  
Local Feature ◽  
Actual State ◽  
Feature Group ◽  
Novel Approach ◽  
Heterogeneous Datasets ◽  
Novel Method ◽  
Scaling Invariant ◽  
Group Matching

The importance of finding correct correspondences between two images is the major aspect in problems such as appearance-based robot localization and content-based image retrieval. Local feature matching has become a commonly used method to compare images, despite being highly probable that at least some of the matchings/correspondences it detects are incorrect. In this paper, we describe a novel approach to local feature matching, named Feature Group Matching (FGM), to select stable features and obtain a more reliable similarity value between two images. The proposed technique is demonstrated to be translational, rotational and scaling invariant. Experimental evaluation was performed on large and heterogeneous datasets of images using SIFT and SURF, the actual state-of-the-art feature extractors. Results show that FGM avoids almost 95% of incorrect matchings, reduces the visual aliasing (number of images considered similar) and increases both robotic localization and image retrieval accuracy on the average of 13%.

scholarly journals RoRD: Rotation-Robust Descriptors and Orthographic Views for Local Feature Matching

2021 ◽  
Author(s):  
Udit Singh Parihar ◽  
Aniket Gujarathi ◽  
Kinal Mehta ◽  
Satyajit Tourani ◽  
Sourav Garg ◽  
...  
Keyword(s):  
Feature Matching ◽  
Local Feature
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