Vitality Assessment of Boar Sperm Using an Adaptive LBP Based on Oriented Deviation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Oscar García-Olalla , Enrique Alegre , Laura Fernández-Robles , María Teresa García-Ordás

ABSTRACT

A new method to describe sperm vitality using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation of the images and hence it has been called ALBPS. Regarding semen vitality assessment, ALBPS outperformed previous literature works with an 81.88% of accuracy and it also yielded higher hit rates than the LBP and ALBP base-line methods. Concerning the global description of sperm heads, several classical texture algorithms were tested and a descriptor based on Wavelet transform and Haralick feature extraction (WCF13) obtained the best results. Both local and global descriptors were combined and the classification was carried out with a Support Vector Machine. Therefore, our proposal is novel in three ways. First, a new local feature extraction method ALBPS is introduced. Second, a hybrid method combining the proposed local ALBPS and a global descriptor is presented outperforming our first approach and all other methods evaluated for this problem. Third, vitality classification accuracy is greatly improved with the two former texture descriptors presented. F-Score and accuracy values were computed in order to measure the performance. The best overall result was yielded by combining ALBPS with WCF13 reaching a F-Score equals to 0.886 and an accuracy of 85.63%. More... »

PAGES

61-72

Book

TITLE

Computer Vision - ACCV 2012 Workshops

ISBN

978-3-642-37409-8
978-3-642-37410-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-37410-4_6

DOI

http://dx.doi.org/10.1007/978-3-642-37410-4_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002800980


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