Publications

@article{gonzalez-castro_pixel_2014,

title = {Pixel classification using general adaptive neighborhood-based features},

author = {Víctor González-Castro and Johan Debayle and Vladimir Curic},

url = {https://ieeexplore.ieee.org/abstract/document/6977356},

year  = {2014},

date = {2014-01-01},

journal = {2014 22nd International Conference on Pattern Recognition},

pages = {3750–3755},

abstract = {This paper presents a new descriptor using General Adaptive Neighborhoods (GANs) for classifying pixels in texture images. GANs define a spatial region around each pixel that fits its local structure, and pixel features are derived from region-based and intensity-based measurements. The method outperforms others, achieving 97.25% accuracy in five-class classification and high area under curve values in binary classifications using the VisTex database.},

note = {Publisher: IEEE},

keywords = {Adaptive Neighborhoods, mathematical morphology, pixel classification, texture classification},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_adaptive_2011,

title = {Adaptive filters evaluation for sharpness enhancement and noise removal},

author = {Óscar García-Olalla Olivera and Diego García-Ordás and Maite García-Ordás and Laura Fernández-Robles and Enrique Alegre},

url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/229828217_Adaptive_filters_evaluation_for_sharpness_enhancement_and_noise_removal/links/544a1e150cf2ea6541343976/Adaptive-filters-evaluation-for-sharpness-enhancement-and-noise-removal.pdf},

year  = {2011},

date = {2011-01-01},

journal = {Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011},

pages = {55},

abstract = {In this work, two adaptive filter algorithms for sharpness enhancement and noise removal have been evaluated. A modification to the Adaptive Bilateral Filter (ABF) method have been carried out using the difference of gaussians as the pixel classification algorithm. A metric comparison with adaptive Non lineal Complex Diffusion Filter (NCDF) algorithm has been applied using three methods: MSE (mean square error), ENL (equivalent number of looks) and CNR (Contrast to noise ratio). Results showed that the proposed modification outperforms all the others using the MSE, CNR and ENL (in light areas) criteria. However, the Adaptive NCDF obtains the best result in the dark areas of the image.},

note = {Publisher: Universidad de Sevilla},

keywords = {adaptive bilateral filter, CNR, ENL, image metrics, image processing, image quality comparison, MSE, noise removal, non-linear filters, pixel classification, sharpness enhancement},

pubstate = {published},

tppubtype = {article}

}