2018/03/22 by Hunter Goforth, Simon Lucey, Goforth, Hunter +1
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.1803.08542
openalex publication_date 2018/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a method of temporally-invariant image registration for outdoor scenes, with invariance across time of day, across seasonal variations, and across decade-long periods, for low- and high-texture scenes. Our method can be useful for applications in remote sensing, GPS-denied UAV localization, 3D reconstruction, and many others. Our method leverages a recently proposed approach to image registration, where fully-convolutional neural networks are used to create feature maps which can be registered using the Inverse-Composition Lucas-Kanade algorithm (ICLK). We show that invariance that is learned from satellite imagery can be transferable to time-lapse data captured by webcams mounted on buildings near ground-level.