Complex representations for learning statistical shape priors

Parametrisation of the shape of deformable objects is of paramount importance in many computer vision applications. Many state-of-the-art statistical deformable models perform landmark localisation via optimising an objective function over a certain parametrisation of the object's shape. Arguab...

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Bibliographic Details
Published in2017 25th European Signal Processing Conference (EUSIPCO) pp. 1180 - 1184
Main Authors Papaioannou, Athanasios, Antonakos, Epameinondas, Zafeiriou, Stefanos
Format Conference Proceeding
LanguageEnglish
Published EURASIP 01.08.2017
Subjects
Computer vision
Covariance matrices
Principal component analysis
Shape
Signal processing
Training
Two dimensional displays
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Summary:Parametrisation of the shape of deformable objects is of paramount importance in many computer vision applications. Many state-of-the-art statistical deformable models perform landmark localisation via optimising an objective function over a certain parametrisation of the object's shape. Arguably, the most popular way is by employing statistical techniques. The points of shape samples of an object lie in a 2D lattice and they are normally represented by concatenating the 2D coordinates into a vector. As the 2D coordinates can be naturally represented as a complex number, in this paper we study statistical complex number representations of an object's shape. In particular, we show that the real representation provides a similar statistical prior as the widely linear complex model, while the circular complex representation results in a much more condensed encoding.
ISSN:2076-1465
DOI:10.23919/EUSIPCO.2017.8081394