Image Matching from Handcrafted to Deep Features: A Survey

By Jiayi Ma, Xingyu Jiang, Aoxiang Fan, Junjun Jiang, Junchi Yan. International Journal of Computer Vision, 2020

932 citations recorded by the source corpus.

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/77a956512e22e37223ac6a00dcf191a086951505

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In Aerospace Engineering

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Citations in the corpus

Grounding Image Matching in 3D with MASt3R (2024, methodology): “Departing from the former handcrafted methods like SIFT [52,71], modern approaches have been shifting towards learning-based data-driven schemes for detecting keypoints [8,60,97,117], describing them [7,33, 37,88] or both at the same time [10,21,53,54,70,98].”
A review of multimodal image matching: Methods and applications (2021, methodology): “General image matching, as defined in related surveys [1,2], aims o identify and then correspond the same or similar structure/content rom two or more images.”
A Review on Deep Learning in UAV Remote Sensing (2021, background): “A recent survey on image matching [129] concluded that this thematic is still an open problem and pointed out the potential of DL is this task.”