Publications

@article{gonzalez-castro_adaptive_2012,

title = {Adaptive pattern spectrum image description using euclidean and geodesic distance without training for texture classification},

author = {Víctor González-Castro and Enrique Alegre and Óscar García-Olalla Olivera and Laura Fernández-Robles and Maria Teresa García-Ordás},

url = {https://digital-library.theiet.org/doi/10.1049/iet-cvi.2012.0098},

year  = {2012},

date = {2012-01-01},

journal = {IET Computer Vision},

volume = {6},

number = {6},

pages = {581–589},

abstract = {Mathematical morphology can be used to extract a shape–size distribution called pattern spectrum (PS) with texture description purposes. However, the structuring element (SE) used to compute it does not vary along the image; and therefore it does not capture its geometrical variations. The author's proposal consists of computing an SE at each pixel whose size and shape varies with two distance criterions: an Geodesic distance and a Euclidean distance, in order to fit the texture as well as possible. Combining the Geodesic and the Euclidean descriptors as just one descriptor, the classification results of several textures from the VisTex and Brodatz database show that this approach outperforms the classical PS, the Geodesic and the Euclidean descriptors separately and, in contrast with other adaptive methods, it does not require previous training.},

note = {Publisher: IET Digital Library},

keywords = {adaptive methods, euclidean distance, geodesic distance, mathematical morphology, pattern spectrum, texture classification},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_adaptive_nodate,

title = {Adaptive texture description for semen vitality assessment},

author = {Víctor González-Castro and Enrique Alegre and Alexci Suárez-Castrillón and Óscar García-Olalla Olivera and María Teresa García-Ordás},

url = {https://d1wqtxts1xzle7.cloudfront.net/47501584/Adaptive_texture_description_for_semen_v20160725-8795-17ysl7b-libre.pdf?1469454941=&response-content-disposition=inline%3B+filename%3DAdaptive_texture_description_for_semen_v.pdf&Expires=1739193493&Signature=SPojfSyGRHX-PAT8i5lgMywJ52650XScp8t74YrAAbRnnZ0mPJbvVLYRGRVG3eNTcH0gHbWwIshfAz3ok9lb8gwRdrSSDSazEnRN5cYx8eBGm1rhAQ0WmI7Cwc0TaLMt1nk41LMRM4hROUQEF9P6YZShfswvVnj~e9IXqGrjksaOYlzuk7Y2zsIzg4jnkgk~w1gJeZ-gxUj~5mdtEESX7zDTdTrbyvs4Roiyoig8~jgMdtkCptWrABzqnovahSK9D-vjedWjo-EVaVue22Wa-k1z7hSSerV1X~AZt68BQqa2GqhwOxQ~qfJdpU4j8o88~hi6RcbTtsGj7-BE9vyWZA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},

abstract = {In this paper an adaptive texture description method based on granulometry 

is proposed. It is a variant of the Pattern Spectrum, and both the adaptive 

and the ordinary descriptors are used to classify boar spermatozoa into 

dead or alive. A set of 845 boar spermatozoon heads were assessed and a 

back-propagation neural network was used in order to classify them. We 

have described both the original grey level images and the same images 

after applying a range texture filter on them. The best hit rates have been 

obtained when the adaptive Pattern Spectrum was used to describe the 

filtered images.},

keywords = {boar semen, granulometry, pattern spectrum, texture description},

pubstate = {published},

tppubtype = {article}

}