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Author  |
C. Alejandro Parraga; Ramon Baldrich; Maria Vanrell |
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Title |
Accurate Mapping of Natural Scenes Radiance to Cone Activation Space: A New Image Dataset |
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2010 |
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5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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50–57 |
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The characterization of trichromatic cameras is usually done in terms of a device-independent color space, such as the CIE 1931 XYZ space. This is indeed convenient since it allows the testing of results against colorimetric measures. We have characterized our camera to represent human cone activation by mapping the camera sensor's (RGB) responses to human (LMS) through a polynomial transformation, which can be “customized” according to the types of scenes we want to represent. Here we present a method to test the accuracy of the camera measures and a study on how the choice of training reflectances for the polynomial may alter the results. |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CAT @ cat @ PBV2010a |
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1322 |
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C. Alejandro Parraga; Robert Benavente; Maria Vanrell |
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Title |
Towards a general model of colour categorization which considers context |
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2010 |
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Perception. ECVP Abstract Supplement |
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PER |
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39 |
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86 |
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In two previous experiments [Parraga et al, 2009 J. of Im. Sci. and Tech 53(3) 031106; Benavente et al,2009 Perception 38 ECVP Supplement, 36] the boundaries of basic colour categories were measured.
In the first experiment, samples were presented in isolation (ie on a dark background) and boundaries were measured using a yes/no paradigm. In the second, subjects adjusted the chromaticity of a sample presented on a random Mondrian background to find the boundary between pairs of adjacent colours.
Results from these experiments showed significant di erences but it was not possible to conclude whether this discrepancy was due to the absence/presence of a colourful background or to the di erences in the paradigms used. In this work, we settle this question by repeating the first experiment (ie samples presented on a dark background) using the second paradigm. A comparison of results shows that
although boundary locations are very similar, boundaries measured in context are significantly di erent(more di use) than those measured in isolation (confirmed by a Student’s t-test analysis on the subject’s answers statistical distributions). In addition, we completed the mapping of colour name space by measuring the boundaries between chromatic colours and the achromatic centre. With these results we
completed our parametric fuzzy-sets model of colour naming space. |
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CAT @ cat @ PBV2010b |
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1326 |
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C. Alejandro Parraga; Robert Benavente; Maria Vanrell |
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Title |
Modeling Colour-Naming Space with Fuzzy Sets |
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2007 |
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Perception 36:198–198, supp |
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CAT @ cat @ PBV2007 |
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843 |
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C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Title |
Psychophysical measurements to model inter-colour regions of colour-naming space |
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2009 |
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Journal of Imaging Science and Technology |
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53 |
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3 |
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031106 (8 pages) |
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image processing; Analysis |
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JCR Impact Factor 2009: 0.391
In this paper, we present a fuzzy-set of parametric functions which segment the CIE lab space into eleven regions which correspond to the group of common universal categories present in all evolved languages as identified by anthropologists and linguists. The set of functions is intended to model a color-name assignment task by humans and differs from other models in its emphasis on the inter-color boundary regions, which were explicitly measured by means of a psychophysics experiment. In our particular implementation, the CIE lab space was segmented into eleven color categories using a Triple Sigmoid as the fuzzy sets basis, whose parameters are included in this paper. The model’s parameters were adjusted according to the psychophysical results of a yes/no discrimination paradigm where observers had to choose (English) names for isoluminant colors belonging to regions in-between neighboring categories. These colors were presented on a calibrated CRT monitor (14-bit x 3 precision). The experimental results show that inter- color boundary regions are much less defined than expected and color samples other than those near the most representatives are needed to define the position and shape of boundaries between categories. The extended set of model parameters is given as a table. |
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CAT @ cat @ PBV2009 |
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1157 |
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Author |
C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Title |
Modelling Inter-Colour Regions of Colour Naming Space |
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2008 |
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4th European Conference on Colour in Graphics, Imaging and Vision Proceedings |
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218–222 |
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Terrassa (Spain) |
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CGIV08 |
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CAT;CIC |
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CAT @ cat @ PBV2008 |
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969 |
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Author |
C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia |
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Title |
Modelling symmetry perception with banks of quadrature convolutional Gabor kernels |
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Conference Article |
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2019 |
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42nd edition of the European Conference on Visual Perception |
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224-224 |
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Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation. |
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Leuven; Belgium; August 2019 |
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ECVP |
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NEUROBIT; 600.128 |
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Admin @ si @ POA2019 |
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3371 |
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Author |
C. Butakoff; Simone Balocco; F.M. Sukno; C. Hoogendoorn; C. Tobon-Gomez; G. Avegliano; A.F. Frangi |
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Title |
Left-ventricular Epi- and Endocardium Extraction from 3D Ultrasound Images Using an Automatically Constructed 3D ASM |
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Journal Article |
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2016 |
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Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization |
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CMBBE |
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4 |
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5 |
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265-280 |
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ASM; cardiac segmentation; statistical model; shape model; 3D ultrasound; cardiac segmentation |
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In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach. |
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2168-1163 |
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MILAB |
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Admin @ si @ BBS2016 |
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2449 |
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C. Cortes |
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Title |
Definicio d´un sensor de visio artificial per a l´ajust automatic de tintes en la impressio de pape |
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2001 |
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CVC Technical Report #53 |
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Admin @ si @ Cor2001 |
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184 |
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C. Gratin; Jordi Vitria; F. Moreso; D. Seron |
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Texture Classification using Neural Networks and Local Granulometries |
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1994 |
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EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors |
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309-316 |
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Neural Networks; Granulometry; Kidney; Texture; Classication |
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Fointanebleau, France |
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OR;MV |
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BCNPCL @ bcnpcl @ GVM1994 |
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110 |
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C. Mariño; M.G. Penas; M. Penedo; David Lloret; M.J. Carreira |
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Integration of Mutual Information and Creaseness Based Methods for the Automatic Registration of SLO Sequences. |
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2001 |
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Proceedings of the SIARP´2001. |
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197 |
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C. Mariño; V.M. Gulias; M.G. Penas; M. Penedo; Victor Leboran; A. Mosquera; M.J. Carreira; David Lloret |
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Sistema de Interpretacion Automatica de Secuencias solo Basado en un Servidor vod. |
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2001 |
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Proceedings of the SIT2001. |
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196 |
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C. Molina; G.P. Prause; Petia Radeva; M. Sonka |
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Title |
Catheter Path Reconstruction from Biplane Angiography using 3D Snakes. |
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1998 |
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SPIE – Medical imaging. |
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San Diego; CA; USA |
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BCNPCL @ bcnpcl @ MPR1998 |
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22 |
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C. Molina; J.B. Subirana |
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Reduction of complexity for object recognition algorithms |
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1995 |
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CVC Technical Report #03 |
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Admin @ si @ MoS1995a |
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225 |
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C. Molina; J.B. Subirana |
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Polynomial-Time Algorithm for 2D object recognition. |
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1995 |
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VI National Simposium on Pattern Recognition and image Analysis |
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Cordoba |
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231 |
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C. Padres |
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Aplicaciones al modelo de formas activas para un entorno aumentado |
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2000 |
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CVC Technical Report #46 |
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no |
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Admin @ si @ Pad2000 |
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347 |
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