Dense versus Sparse Approaches for Estimating the Fundamental Matrix

There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimise...

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Vydané v:	International journal of computer vision Ročník 96; číslo 2; s. 212 - 234
Hlavní autori:	Valgaerts, Levi, Bruhn, Andrés, Mainberger, Markus, Weickert, Joachim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.01.2012 Springer Springer Nature B.V
Predmet:	Algorithms Analysis Applied sciences Artificial Intelligence Computer Imaging Computer Science Computer science; control theory; systems Computer vision Correspondence Energy Energy use Estimates Estimating Exact sciences and technology Feature based Geometry Image Processing and Computer Vision Image processing systems Machine vision Methods Optimization Partial differential equations Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry State of the art Strategy Studies Variational methods Vision Vision systems Performance evaluation 3D reconstruction Fundamental matrix Optical flow Energy method Computer vision Image processing Correspondence principle Modeling Image reconstruction Problem solving Variational calculus Tridimensional image
ISSN:	0920-5691, 1573-1405
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