Abstract
Imaging systems have an indisputable role in revealing vegetation posture under diverse flow conditions, image sequences being generated with off the shelf digital cameras. Such sensors are cheap but introduce a range of distortion effects, a trait only marginally tackled in hydraulic studies focusing on water-vegetation dependencies. This paper aims to bridge this gap by presenting a simple calibration method to remove both camera lens distortion and refractive effects of water. The effectiveness of the method is illustrated using the variable projected area, computed for both simple and complex shaped objects. Results demonstrate the significance of correcting images using a combined lens distortion and refraction model, prior to determining projected areas and further data analysis. Use of this technique is expected to increase data reliability for future work on vegetated channels.
Highlights
The capability of aquatic plants to deform and “reconfigure” is critical to the functioning of lotic ecosystems [1,2]
Different setups and image perspectives have been adopted to estimate A f or equivalent descriptors, ranging from: mirrors attached to the bottom part of laboratory facilities or in situ environments combined with top view images using regular cameras [4,5,6]; images acquired in still air [7] and water conditions [8,9]; to submerged digital cameras aligned with the plant mass centre [10,11]
Non-metric sensors such as consumer grade digital cameras do not possess, as opposed to photogrammetric or metric cameras, a calibration certificate. This demands deriving a set of parameters which can be used to describe the internal geometry of the imaging system [12]
Summary
The capability of aquatic plants to deform and “reconfigure” is critical to the functioning of lotic ecosystems [1,2]. Non-metric sensors such as consumer grade digital cameras do not possess, as opposed to photogrammetric or metric cameras, a calibration certificate This demands deriving a set of parameters which can be used to describe the internal geometry of the imaging system (e.g., focal length, principal point offset, and radial and tangential lens distortion) [12]. This step is crucial, notably if precise spatial information is to be extracted and carried out through a process known as “self-calibrating bundle adjustment” [13,14]. Ocnoounnrs-emwqeuoterrnickceipmorafoguvesreeysr. uWthnhaaiwtttaaarkesenrime[s1sp5]olefstimamteaesgtihtnhogadtgioenolomthgeeytr,aybesoaersniacleyporaofdcaeodkpuntroaewbtnolefaopbcpyarloelpexnrpigaettherl,iymdciesatnloibrttariaoltinests, allows for an effeecffteicvtsecacnanmotebrea sccraultiibnrizaetdioann,d Wthuunsdeernetaabl.li[n10g] arsesfiumneedm, wenitthoouft aepxpiasrteinntgreeasxopne, rthimat ecanmtaelraprotocols, particularldyistthorotisoen pefrfecvtas iwlienrge mininliambizoerdatinortyhe-birawseodrk.aBcetaivriintgieisn. mTinhde tpherseeseconntsiadneraaltyiosnis, itshirsepsatpriecrted to the parameterpprersoejnetcstead mareethaoddubeasteoditosnrewleevlla-ensctaebliinshaedqupahtoictosgtruamdimeestr(iec.gp.,ritnocipelveas lutoateelitmhienadteralgencsoefficient) but concludcsaiisloitbonrrastitoesndteinmcbammotehirndagsr.yfarOnoudmrwetwthoeinrskvwirpoonrromkveenastrsetahneadtqucoaamllspyiamrvepsaleplirdomjefecottheroddoaotrhleoaegsryu,msienoagrsinplyohno-acldaologibpirtcaaatbelldesatnubdydies using similar imaexgpinergimseynstatelimstss, .allows for an effective camera calibration, enabling refinement of existing
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