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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2006). A data set for fuzzy colour naming. Color Research & Application, 31(1):48–56.
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Josep Llados. (2007). Advances in Graphics Recognition. In Digital Document Processing, Major Directions and Recent Advances, Advances in Pattern Recognition, B.B. Chaudhuri, ed., 281–304.
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Firat Ismailoglu, Ida G. Sprinkhuizen-Kuyper, Evgueni Smirnov, Sergio Escalera, & Ralf Peeters. (2015). Fractional Programming Weighted Decoding for Error-Correcting Output Codes. In Multiple Classifier Systems, Proceedings of 12th International Workshop , MCS 2015 (pp. 38–50). Springer International Publishing.
Abstract: In order to increase the classification performance obtained using Error-Correcting Output Codes designs (ECOC), introducing weights in the decoding phase of the ECOC has attracted a lot of interest. In this work, we present a method for ECOC designs that focuses on increasing hypothesis margin on the data samples given a base classifier. While achieving this, we implicitly reward the base classifiers with high performance, whereas punish those with low performance. The resulting objective function is of the fractional programming type and we deal with this problem through the Dinkelbach’s Algorithm. The conducted tests over well known UCI datasets show that the presented method is superior to the unweighted decoding and that it outperforms the results of the state-of-the-art weighted decoding methods in most of the performed experiments.
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Jian Yang, Zhong Jin, Jing-Yu Yang, David Zhang, & Alejandro F. Frangi. (2004). Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recognition, 37(10): 2097–2100 (IF: 2.176).
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Yong Xu, Jing-Yu Yang, & Zhong Jin. (2004). A novel method for Fisher discriminant analysis. Pattern Recognition, 37(2):381–384 (IF: 2.176).
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Debora Gil, Jaume Garcia, Manuel Vazquez, Ruth Aris, & Guillaume Houzeaux. (2008). Patient-Sensitive Anatomic and Functional 3D Model of the Left Ventricle Function. In 8th World Congress on Computational Mechanichs (WCCM8)/5th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2008). Venezia (Italia).
Abstract: Early diagnosis and accurate treatment of Left Ventricle (LV) dysfunction significantly increases the patient survival. Impairment of LV contractility due to cardiovascular diseases is reflected in its motion patterns. Recent advances in medical imaging, such as Magnetic Resonance (MR), have encouraged research on 3D simulation and modelling of the LV dynamics. Most of the existing 3D models consider just the gross anatomy of the LV and restore a truncated ellipse which deforms along the cardiac cycle. The contraction mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fibers. It follows that such simplified models do not allow evaluation of the heart electro-mechanical function and coupling, which has recently risen as the key point for understanding the LV functionality . In order to thoroughly understand the LV mechanics it is necessary to consider the complete anatomy of the LV given by the orientation of the myocardial fibres in 3D space as described by Torrent Guasp. We propose developing a 3D patient-sensitive model of the LV integrating, for the first time, the ven- tricular band anatomy (fibers orientation), the LV gross anatomy and its functionality. Such model will represent the LV function as a natural consequence of its own ventricular band anatomy. This might be decisive in restoring a proper LV contraction in patients undergoing pace marker treatment. The LV function is defined as soon as the propagation of the contractile electromechanical pulse has been modelled. In our experiments we have used the wave equation for the propagation of the electric pulse. The electromechanical wave moves on the myocardial surface and should have a conductivity tensor oriented along the muscular fibers. Thus, whatever mathematical model for electric pulse propa- gation [4] we consider, the complete anatomy of the LV should be extracted. The LV gross anatomy is obtained by processing multi slice MR images recorded for each patient. Information about the myocardial fibers distribution can only be extracted by Diffusion Tensor Imag- ing (DTI), which can not provide in vivo information for each patient. As a first approach, we have computed an average model of fibers from several DTI studies of canine hearts. This rough anatomy is the input for our electro-mechanical propagation model simulating LV dynamics. The average fiber orientation is updated until the simulated LV motion agrees with the experimental evidence provided by the LV motion observed in tagged MR (TMR) sequences. Experimental LV motion is recovered by applying image processing, differential geometry and interpolation techniques to 2D TMR slices [5]. The pipeline in figure 1 outlines the interaction between simulations and experimental data leading to our patient-tailored model.
Keywords: Left Ventricle; Electromechanical Models; Image Processing; Magnetic Resonance.
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Petia Radeva, & Jordi Vitria. (2003). “Inteligencia artificial” Centre de Visio per Computador.
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Antonio Lopez, Ernest Valveny, & Juan J. Villanueva. (2005). Real-time quality control of surgical material packaging by artificial vision. Assembly Automation, 25(3).
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A. Sanfeliu, & Juan J. Villanueva. (2005). An approach of visual motion analysis. PRL - Pattern Recognition Letters, 26(3), 355–368.
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Angel Sappa. (2006). Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation. IEEE Transactions on Image Processing, 15(2):377–384.
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