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Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson |
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Title |
A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation |
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2012 |
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Journal of Vision |
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VSS |
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12 |
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6 (7) |
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1-14 |
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When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data. |
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Admin @ si @ VOV2012 |
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1998 |
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Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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Title |
Low-level SpatioChromatic Grouping for Saliency Estimation |
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2013 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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35 |
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11 |
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2810-2816 |
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We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics. |
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0162-8828 |
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CIC; 600.051; 600.052; 605.203 |
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Admin @ si @ MVO2013 |
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2289 |
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C. Alejandro Parraga; Javier Vazquez; Maria Vanrell |
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A new cone activation-based natural images dataset |
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2009 |
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Perception |
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PER |
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36 |
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180 |
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We generated a new dataset of digital natural images where each colour plane corresponds to the human LMS (long-, medium-, short-wavelength) cone activations. The images were chosen to represent five different visual environments (eg forest, seaside, mountain snow, urban, motorways) and were taken under natural illumination at different times of day. At the bottom-left corner of each picture there was a matte grey ball of approximately constant spectral reflectance (across the camera's response spectrum,) and nearly Lambertian reflective properties, which allows to compute (and remove, if necessary) the illuminant's colour and intensity. The camera (Sigma Foveon SD10) was calibrated by measuring its sensor's spectral responses using a set of 31 spectrally narrowband interference filters. This allowed conversion of the final camera-dependent RGB colour space into the Smith and Pokorny (1975) cone activation space by means of a polynomial transformation, optimised for a set of 1269 Munsell chip reflectances. This new method is an improvement over the usual 3 × 3 matrix transformation which is only accurate for spectrally-narrowband colours. The camera-to-LMS transformation can be recalculated to consider other non-human visual systems. The dataset is available to download from our website. |
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CAT @ cat @ PVV2009 |
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1193 |
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Author |
Olivier Penacchio |
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Title |
Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane |
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2011 |
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Mathematische Nachrichten |
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MN |
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284 |
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4 |
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526-542 |
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Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25 |
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We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim |
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WILEY-VCH Verlag |
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R. Mennicken |
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1522-2616 |
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Admin @ si @ Pen2011 |
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1721 |
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David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich |
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Traffic sign recognition for computer vision project-based learning |
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2013 |
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IEEE Transactions on Education |
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T-EDUC |
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56 |
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3 |
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364-371 |
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traffic signs |
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This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. |
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0018-9359 |
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Admin @ si @ GSL2013; ADAS @ adas @ |
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2160 |
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