Publications

@article{fernandez-robles_recognition_2017,

title = {Recognition and retrieval of objects in diverse applications},

author = {Laura Fernández-Robles},

url = {https://www.raco.cat/index.php/ELCVIA/article/view/v16-n2-fernandez},

year  = {2017},

date = {2017-01-01},

journal = {ELCVIA: electronic letters on computer vision and image analysis},

volume = {16},

number = {2},

pages = {21–24},

abstract = {This work focuses on object description and retrieval techniques applied to various real-world problems. It explores the classification of boar spermatozoa based on acrosome integrity using methods based on invariant local features. The paper also presents solutions for insert localization and recognition of broken inserts in milling heads, offering an automatic, in-process method for detection without interrupting machining operations. Additionally, it introduces a new descriptor, colour COSFIRE, for object retrieval in the context of the European project aimed at combating sexual exploitation of children.},

keywords = {COSFIRE Descriptor, Invariant Features, machine vision, Object Retrieval, Spermatozoa Classification},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_boar_2011,

title = {Boar spermatozoa classification using longitudinal and transversal profiles (LTP) descriptor in digital images},

author = {Enrique Alegre and Óscar García-Olalla Olivera and Víctor González-Castro and Swapna Joshi},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21073-0_36},

year  = {2011},

date = {2011-01-01},

journal = {Combinatorial Image Analysis: 14th International Workshop, IWCIA 2011, Madrid, Spain, May 23-25, 2011. Proceedings 14},

pages = {410–419},

abstract = {A new textural descriptor called Longitudinal and Transversal Profiles (LTP) has been proposed to classify images of dead and alive spermatozoa heads. The dataset consists of 376 dead spermatozoa head images and 472 alive ones. The performance of LTP was compared to other descriptors like Pattern Spectrum, Flusser, Hu, and a histogram-based statistical descriptor. The feature vectors were classified using both a back-propagation Neural Network and the kNN algorithm. The LTP descriptor achieved a classification error of 30.58%, outperforming the other descriptors. Additionally, the Area Under the ROC Curve (AUC) confirmed that LTP provided better performance than the other texture descriptors.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Image classification, kNN, LTP Descriptor, Neural Network, Spermatozoa Classification, texture descriptors},

pubstate = {published},

tppubtype = {article}

}