Manhattan Frame Detection in Lens  Distorted Images

Liepieshov, Kostiantyn

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dc.contributor.author	Liepieshov, Kostiantyn
dc.date.accessioned	2024-02-15T09:16:45Z
dc.date.available	2024-02-15T09:16:45Z
dc.date.issued	2020
dc.identifier.citation	Liepieshov, Kostiantyn. Manhattan Frame Detection in Lens Distorted Images / Liepieshov, Kostiantyn; Supervisor: Rostyslav Hryniv, James Pritts; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2020. – 39 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4510
dc.language.iso	en	uk
dc.title	Manhattan Frame Detection in Lens Distorted Images	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Camera auto-calibration from a single image with radial distortion is a prevalent task in computer vision. Most of the existing approaches are based on the same process of extraction of features, such as circles, from the image. Since those features are noisy, the error is propagated to the higher level, and the final estimations are inaccurate. We incorporate the constraints imposed by the division model of radial distor- tion and suggest a simple approach that gives soft estimates of three Manhattan di- rections. For this task, we adapt a well-known Expectation Maximisation algorithm. We combine it with different initialization and filtering steps that we form based on the division model and Manhattan world assumptions. We illustrate the performance of the proposed approach on YORK Urban Database (YUD) and AIT Dataset of indoor and outdoor scenes. Besides, we experiment with the proposed initializations and filtering procedures.	uk