JOURNAL ARTICLE

Scale Invariant Feature Transform Based Image Matching and Registration

Abstract

This paper presents Image matching and registration method that is invariant to scale, rotation, translation and illumination changes. The method is named as Scale Invariant Feature Transform (SIFT). This algorithm will detect and describe image features such as contours, points, corners etc. SIFT descriptors are the characteristic signature of the feature. The features calculated from the image to be registered should be distinctive and then it can be matched. It can be useful in object recognition, image mosaicing, 3 D reconstruction and video tracking. The simulation results shows that this algorithm works well in all types of cases having scale and rotation difference, it also register the object having occlusion and clutter background.

Keywords:
Scale-invariant feature transform Artificial intelligence Computer vision Clutter Computer science Image registration Pattern recognition (psychology) Invariant (physics) Rotation (mathematics) Translation (biology) Feature (linguistics) Feature matching Image matching Feature extraction Cognitive neuroscience of visual object recognition Matching (statistics) Image (mathematics) Mathematics Radar

Metrics

23
Cited By
0.72
FWCI (Field Weighted Citation Impact)
5
Refs
0.77
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Robotics and Sensor-Based Localization
Physical Sciences →  Engineering →  Aerospace Engineering
Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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