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Xavier Otazu, & Oriol Pujol. (2006). Wavelet based approach to cluster analysis. Application on low dimensional data sets. PRL - Pattern Recognition Letters, 27(14), 1590–1605.
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Debora Gil, & Petia Radeva. (2006). Inhibition of false landmarks. PRL - Pattern Recognition Letters, 27(9), 1022–1030.
Abstract: Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.
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Jaume Amores, N. Sebe, & Petia Radeva. (2006). Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier. PRL - Pattern Recognition Letters, 27(3), 201–209.
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Cristina Cañero, & Petia Radeva. (2003). Vesselness enhancement diffusion. PRL - Pattern Recognition Letters, 24(16), 3141–3151.
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Francesco Ciompi, Oriol Pujol, & Petia Radeva. (2014). ECOC-DRF: Discriminative random fields based on error correcting output codes. PR - Pattern Recognition, 47(6), 2193–2204.
Abstract: We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.
Keywords: Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models
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