We present an effective method for the matching of multimodal images. Accurate image matching is the basis of various applications, such as image registration and structure from motion. Conventional matching methods fail when handling noisy multimodal image pairs with severe scale change, rotation, and nonlinear intensity distortion (NID). Toward this need, we introduce an image pyramid strategy to tackle scale change. We put forward an accurate primary orientation estimation approach to reduce the effect of image rotation at any angle. We utilize multi-scale and multi-orientation image filtering results and a feature-to-template matching scheme to ensure effective and accurate matching under large NID. Integrating these improvements significantly increases noise, scale, rotation, and NID invariant capability. Our experimental results confirm the excellent ability to achieve high-quality matches across various multimodal images. The proposed method outperforms the mainstream multimodal image matching methods in qualitative and quantitative evaluations. Our implementation is available at https://github.com/Zhongli-Fan/NISR.
R2FD2: Fast and Robust Matching of Multimodal Remote Sensing Image via Repeatable Feature Detector and Rotation-invariant Feature Descriptor
Southwest Jiaotong University（西南交通大学）；State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University（高速铁路安全空间信息技术省部共建工程实验室，西南交通大学）；Xinyang Normal University（信阳师范学院）；Northwest Institute of Nuclear Technology（西北核技术研究所）
Multimodal Registration of Retinal Images Using Domain-Specific Landmarks and Vessel Enhancement
CITIC-Research Center of Information and Communication Technologies, University of A Coruña（科蒂克信息与通信技术研究中心，拉科鲁尼亚大学）；Department of Computer Science, University of A Coruña（拉科鲁尼亚大学计算机科学系）；University of A Coruña（拉科鲁尼亚大学）；CITIC-Research Center of Information and Communication Technologies（科蒂克信息与通信技术研究中心）
RoES: Rotational Equivariant Selective-frequency Fusion for Multimodal Images
Zhongnan University of Economics and Law（中南财经政法大学）；City University of Macau（澳门城市大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Hubei University（湖北大学）；Xi’an High-tech Research Institute（西安高新技术研究所）；China University of Geosciences (Wuhan)（中国地质大学（武汉））
A 3D Multimodal Feature for Infrastructure Anomaly Detection
Department of Engineering Science, University of Oxford（工程科学系，牛津大学）；Department of Geotechnical Engineering, College of Civil Engineering, Tongji University（地质工程系，同济大学土木学院）；Construction Engineering, University of Cambridge（建设工程，剑桥大学）
Multimodal Image Matching based on Frequency-domain Information of Local Energy Response
School of Automation, China University of Geosciences（中国地质大学自动化学院）；Hubei Key Laboratory of Advanced Control（湖北省先进控制重点实验室）；Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education（教育部智能地学探测技术工程研究中心）；Faculty of Engineering, China University of Geosciences（中国地质大学工程学院）；Electronic Information School, Wuhan University（武汉大学电子信息学院）
Progressive Multimodal Interaction Network for Reliable Quantification of Fish Feeding Intensity in Aquaculture
National Innovation Center for Digital Fishery（国家数字渔业创新中心）；Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs（农业农村部水产动物与畜禽智慧养殖技术重点实验室）；State Key Laboratory of Efficient Utilization of Agricultural Water Resources（农业水资源高效利用全国重点实验室）；Beijing Engineering and Technology Research Center for Internet of Things in Agriculture（北京市农业物联网工程技术研究中心）；Key Laboratory of Digital Fisheries of Shandong Province（山东省数字渔业重点实验室）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution
The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）；Yale University（耶鲁大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
