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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Published in | 2017 25th European Signal Processing Conference (EUSIPCO) pp. 1180 - 1184 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
EURASIP
01.08.2017
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Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 2076-1465 |
DOI: | 10.23919/EUSIPCO.2017.8081394 |