CorNet: Unsupervised Deep Homography Estimation for Agricultural Aerial Imagery

Autor:	Dewi Endah Kharismawati, Hadi Ali Akbarpour, Filiz Bunyak, Rumana Aktar, Kannappan Palaniappan, Toni Kazic
Rok vydání:	2020
Předmět:	Estimation Accurate estimation Computer science business.industry Deep learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Grid Aerial imagery Metadata Computer vision Artificial intelligence business Planar homography Homography (computer vision)
Zdroj:	Computer Vision – ECCV 2020 Workshops ISBN: 9783030654139 ECCV Workshops (6)
DOI:	10.1007/978-3-030-65414-6_28
Popis:	Efficient and accurate estimation of homographies among images is the first step in mosaicking crop fields for phenotyping. The current strategy uses sophisticated vehicles that have excellent telemetry to hover over a grid of waypoints, imaging each one. This approach simplifies homography estimation, but precludes more flexible, adaptive protocols that can collect richer information. It also makes aerial phenotyping impractical for many researchers and farmers. We are developing an alternative strategy that uses consumer-grade vehicles, freely flown over a variety of trajectories, to collect video. We have developed an unsupervised deep learning network that estimates the sequence of planar homography matrices of our corn fields from imagery, without using any metadata to correct estimation errors. The vehicle was freely flown using a variety of trajectories and camera views. Our system, CorNet, performed faster than and with comparable accuracy to the gold standard ASIFT algorithm in many challenging cases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::aa7370138566165c14e1064bec320074 https://doi.org/10.1007/978-3-030-65414-6_28 Zobrazit plný text záznamu