Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
Estimating class proportions in boar semen analysis using the hellinger distance
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Texture and moments-based classification of the acrosome integrity of boar spermatozoa images
Computer Methods and Programs in Biomedicine
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
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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%.