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Mohammad Rouhani, Angel Sappa, & E. Boyer. (2015). Implicit B-Spline Surface Reconstruction. TIP - IEEE Transactions on Image Processing, 24(1), 22–32.
Abstract: This paper presents a fast and flexible curve, and surface reconstruction technique based on implicit B-spline. This representation does not require any parameterization and it is locally supported. This fact has been exploited in this paper to propose a reconstruction technique through solving a sparse system of equations. This method is further accelerated to reduce the dimension to the active control lattice. Moreover, the surface smoothness and user interaction are allowed for controlling the surface. Finally, a novel weighting technique has been introduced in order to blend small patches and smooth them in the overlapping regions. The whole framework is very fast and efficient and can handle large cloud of points with very low computational cost. The experimental results show the flexibility and accuracy of the proposed algorithm to describe objects with complex topologies. Comparisons with other fitting methods highlight the superiority of the proposed approach in the presence of noise and missing data.
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Katerine Diaz, Francesc J. Ferri, & W. Diaz. (2015). Incremental Generalized Discriminative Common Vectors for Image Classification. TNNLS - IEEE Transactions on Neural Networks and Learning Systems, 26(8), 1761–1775.
Abstract: Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.
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Miguel Oliveira, Angel Sappa, & Victor Santos. (2015). A probabilistic approach for color correction in image mosaicking applications. TIP - IEEE Transactions on Image Processing, 14(2), 508–523.
Abstract: Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
Keywords: Color correction; image mosaicking; color transfer; color palette mapping functions
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Francisco Blanco, Felipe Lumbreras, Joan Serrat, Roswitha Siener, Silvia Serranti, Giuseppe Bonifazi, et al. (2014). Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy. JBiO - Journal of Biomedical Optics, 19(12), 126004–1 - 126004–9.
Abstract: The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories.
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Aura Hernandez-Sabate, Meritxell Joanpere, Nuria Gorgorio, & Lluis Albarracin. (2015). Mathematics learning opportunities when playing a Tower Defense Game. IJSG - International Journal of Serious Games, 57–71.
Abstract: A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes.
Keywords: Tower Defense game; learning opportunities; mathematics; problem solving; game design
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