Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming
By Jiayi Ma, Huabing Zhou, Ji Zhao, Yuan Gao, Junjun Jiang, Jinwen Tian. IEEE Transactions on Geoscience and Remote Sensing, 2015
457 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/f2374add6087ad4509b670f1e2b056ddecaa33e7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lorenzo Bruzzone
- Francesca Bovolo
- Jiayi Ma
- Jie Shan
- Junchi Yan
- Alan L. Yuille
- Yongjun Zhang
- Junjun Jiang
In Computer Vision and Pattern Recognition
In Media Technology
- Jiayi Ma
- Francesca Bovolo
- Lorenzo Bruzzone
- Xiaojie Guo
- Zhenfeng Shao
- Yongjun Zhang
- Guobao Xiao
- Junjun Jiang
Citations in the corpus
- Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion (2016, background): “Hence, in addition to high-resolution remote sensing image retrieval, the proposed unsupervised features would probably benefit other tasks in computer vision such as feature matching [45,46], image fusion [47], and target detection [48].”
- Image Matching from Handcrafted to Deep Features: A Survey (2020, methodology): “D case, six representative methods, namely, TPS-RPM (Chui and Rangarajan 2003), GMM (Jian and Vemuri 2011), CPD (Myronenko and Song 2010), L2E (Ma et al. 2013b), PRGLS (Ma et al. 2015a), and APM (Lian et al. 2017) are evaluated.”
- Non-Rigid Point Set Registration by Preserving Global and Local Structures (2015, background): “F INDING an optimal alignment between two sets of points is a fundamental problem in computer vision, image analysis and pattern recognition [1]–[4].”
- Fast and Robust Matching for Multimodal Remote Sensing Image Registration (2019, methodology): “Area-based methods, sometimes called template matching, achieve correspondences by evaluating the similarity of corresponding window pairs in two images, and select the one with the maximum similarity as the CP pair [23].”
- A review of multimodal image matching: Methods and applications (2021, methodology): “In this experiment, 12 mismatch removal methods are used for comparison, namely, RANSAC [301], MAGSAC++ [317], ICF [323], VFC [319], LLT [325], GS [243], SM [241], LPM [330], GMS [329],”