dc.contributor.authorNg, Felicia Ai Jing
dc.description.abstractGround-based sky cameras are increasingly used now-a-days to understand cloud formation analysis in the atmosphere. Such cloud analysis has varied applications in aviation industry, solar and renewable energy predictions and cloud attenuation analysis. In this report, the author is interested to perform a 3D cloud reconstruction using a pair of ground-based sky cameras. Conventional feature matching approaches in computer vision community could not be directly applied to atmospheric clouds, which often seen as featureless, and that may pose as a challenge in such computer vision analysis. This project aims to identify the most effective feature matching algorithm that could maximise the performance of 3D cloud cells reconstruction. Also, parameters which affect the respective feature matching algorithm performances would be covered. Putting particular focus on various types of clouds, in order to improve the feature point matching performance and efficiency, experimental results on cumulus and dark stratocumulus clouds have reflected that the proposed algorithm of combining SURF feature point matching and adaptive histogram equalization contrast technique, have resulted in stronger robustness in a variety of complex image cases, such as dark clouds and stratocumulus clouds for 3D cloud cell reconstruction.en_US
dc.format.extent102 p.en_US
dc.rightsNanyang Technological University
dc.title3D reconstruction of cloud cellsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLee Yee Huien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US

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