Visible Spectrum and Infra-Red Image Matching: A New Method
Source abstract
Abstract
Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. In the third step, the regression model is used to process the MN-SIFT descriptors of test VS images in order to remove misalignment with the MN-SIFT descriptors of test IR images and to overcome textural and intensity changes. Experiments are performed on two different VS-IR image datasets. The experimental results show that the proposed method works really well and demonstrates on average 14% and 15% better precision and matching scores compared to recently proposed Histograms of Directional Maps (HoDM) descriptor.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering and Computer Sciences, National University of Modern Languages, Islamabad 44000, Pakistan
Faculty of Engineering and Applied Science, University of Regina, Wascana Parkway, Regina, SK S4S 0A2, Canada
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