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Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell |
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Title |
n-Dimensional Distribution Reduction Preserving its Structure |
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2006 |
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Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 |
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IOS Press |
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CAT @ cat @ VTB2006a |
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681 |
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Eduard Vazquez; Ramon Baldrich; Javier Vazquez; Maria Vanrell |
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Title |
Topological histogram reduction towards colour segmentation |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:55–62 |
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Gerona (Spain) |
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CAT @ cat @ VBV2007 |
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809 |
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Author |
Javier Vazquez; Maria Vanrell; Anna Salvatella; Eduard Vazquez |
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Title |
A colour space based on the image content |
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2007 |
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Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press |
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CAT @ cat @ VVS2007 |
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829 |
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Author |
Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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Title |
A System to Segment Text and Symbols from Color Maps |
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2008 |
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Graphics Recognition. Recent Advances and New Opportunities |
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5046 |
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245-256 |
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DAG;CIC |
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no |
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CAT @ cat @ RVL2008 |
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1005 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan; Marc Masana |
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Title |
Interactive Visual and Semantic Image Retrieval |
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2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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48 |
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31-35 |
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One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results. |
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Springer Berlin Heidelberg |
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Angel Sappa; Jordi Vitria |
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1868-4394 |
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978-3-642-35931-6 |
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CIC; 605.203; 600.048 |
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Admin @ si @ WKC2013 |
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2284 |
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Author |
Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus |
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Title |
Color Naming |
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2012 |
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Color in Computer Vision: Fundamentals and Applications |
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17 |
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287-317 |
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John Wiley & Sons, Ltd. |
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Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek |
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Admin @ si @ WBV2012 |
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2063 |
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Author |
Abel Gonzalez-Garcia; Robert Benavente; Olivier Penacchio; Javier Vazquez; Maria Vanrell; C. Alejandro Parraga |
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Title |
Coloresia: An Interactive Colour Perception Device for the Visually Impaired |
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2013 |
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Multimodal Interaction in Image and Video Applications |
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48 |
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47-66 |
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A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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CIC; 600.052; 605.203 |
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Admin @ si @ GBP2013 |
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2266 |
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Author |
C. Alejandro Parraga |
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Title |
Color Vision, Computational Methods for |
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2014 |
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Encyclopedia of Computational Neuroscience |
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1-11 |
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Color computational vision; Computational neuroscience of color |
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The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments. |
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Springer-Verlag Berlin Heidelberg |
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Dieter Jaeger; Ranu Jung |
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978-1-4614-7320-6 |
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CIC; 600.074 |
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Admin @ si @ Par2014 |
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2512 |
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C. Alejandro Parraga |
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Perceptual Psychophysics |
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2015 |
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Biologically-Inspired Computer Vision: Fundamentals and Applications |
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G.Cristobal; M.Keil; L.Perrinet |
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978-3-527-41264-8 |
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CIC; 600.074 |
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Admin @ si @ Par2015 |
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2600 |
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Jordi Roca; A.Owen; G.Jordan; Y.Ling; C. Alejandro Parraga; A.Hurlbert |
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Inter-individual Variations in Color Naming and the Structure of 3D Color Space |
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2011 |
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Journal of Vision |
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VSS |
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12 |
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2 |
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166 |
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36.307
Many everyday behavioural uses of color vision depend on color naming ability, which is neither measured nor predicted by most standardized tests of color vision, for either normal or anomalous color vision. Here we demonstrate a new method to quantify color naming ability by deriving a compact computational description of individual 3D color spaces. Methods: Individual observers underwent standardized color vision diagnostic tests (including anomaloscope testing) and a series of custom-made color naming tasks using 500 distinct color samples, either CRT stimuli (“light”-based) or Munsell chips (“surface”-based), with both forced- and free-choice color naming paradigms. For each subject, we defined his/her color solid as the set of 3D convex hulls computed for each basic color category from the relevant collection of categorised points in perceptually uniform CIELAB space. From the parameters of the convex hulls, we derived several indices to characterise the 3D structure of the color solid and its inter-individual variations. Using a reference group of 25 normal trichromats (NT), we defined the degree of normality for the shape, location and overlap of each color region, and the extent of “light”-“surface” agreement. Results: Certain features of color perception emerge from analysis of the average NT color solid, e.g.: (1) the white category is slightly shifted towards blue; and (2) the variability in category border location across NT subjects is asymmetric across color space, with least variability in the blue/green region. Comparisons between individual and average NT indices reveal specific naming “deficits”, e.g.: (1) Category volumes for white, green, brown and grey are expanded for anomalous trichromats and dichromats; and (2) the focal structure of color space is disrupted more in protanopia than other forms of anomalous color vision. The indices both capture the structure of subjective color spaces and allow us to quantify inter-individual differences in color naming ability. |
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1534-7362 |
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Admin @ si @ ROJ2011 |
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1758 |
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Author |
Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco |
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Brightness induction by contextual influences in V1: a neurodynamical account |
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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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9 |
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Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas and reveals fundamental properties of neural organization in the visual system. Several phenomenological models have been proposed that successfully account for psychophysical data (Pessoa et al. 1995, Blakeslee and McCourt 2004, Barkan et al. 2008, Otazu et al. 2008).
Neurophysiological evidence suggests that brightness information is explicitly represented in V1 and neuronal response modulations have been observed followingluminance changes outside their receptive fields (Rossi and Paradiso, 1999).
In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (1999) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2–3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant effect of contextual influences. In our model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition which makes it possible to recover an image reflecting the perceived intensity. The proposed model successfully accounts for well known pyschophysical effects (among them: the White's and modified White's effects, the Todorović, Chevreul, achromatic ring patterns, and grating induction effects). Our work suggests that intra-cortical interactions in the primary visual cortex could partially explain perceptual brightness induction effects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual pathway. |
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Admin @ si @ OPD2012b |
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2178 |
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Author |
Xavier Otazu |
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Title |
Perceptual tone-mapping operator based on multiresolution contrast decomposition |
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2012 |
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Perception |
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PER |
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41 |
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86 |
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Tone-mapping operators (TMO) are used to display high dynamic range(HDR) images in low dynamic range (LDR) displays. Many computational and biologically inspired approaches have been used in the literature, being many of them based on multiresolution decompositions. In this work, a simple two stage model for TMO is presented. The first stage is a novel multiresolution contrast decomposition, which is inspired in a pyramidal contrast decomposition (Peli, 1990 Journal of the Optical Society of America7(10), 2032-2040).
This novel multiresolution decomposition represents the Michelson contrast of the image at different spatial scales. This multiresolution contrast representation, applied on the intensity channel of an opponent colour decomposition, is processed by a non-linear saturating model of V1 neurons (Albrecht et al, 2002 Journal ofNeurophysiology 88(2) 888-913). This saturation model depends on the visual frequency, and it has been modified in order to include information from the extended Contrast Sensitivity Function (e-CSF) (Otazu et al, 2010 Journal ofVision10(12) 5).
A set of HDR images in Radiance RGBE format (from CIS HDR Photographic Survey and Greg Ward database) have been used to test the model, obtaining a set of LDR images. The resulting LDR images do not show the usual halo or color modification artifacts. |
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0301-0066 |
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CIC |
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Admin @ si @ Ota2012 |
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2179 |
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Author |
Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu |
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Title |
Switching off brightness induction through induction-reversed images |
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2012 |
Publication |
Perception |
Abbreviated Journal |
PER |
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41 |
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208 |
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Brightness induction is the modulation of the perceived intensity of an
area by the luminance of surrounding areas. Although V1 is traditionally regarded as
an area mostly responsive to retinal information, neurophysiological evidence
suggests that it may explicitly represent brightness information. In this work, we
investigate possible neural mechanisms underlying brightness induction. To this end,
we consider the model by Z Li (1999 Computation and Neural Systems10187-212)
which is constrained by neurophysiological data and focuses on the part of V1
responsible for contextual influences. This model, which has proven to account for
phenomena such as contour detection and preattentive segmentation, shares with
brightness induction the relevant effect of contextual influences. Importantly, the
input to our network model derives from a complete multiscale and multiorientation
wavelet decomposition, which makes it possible to recover an image reflecting the
perceived luminance and successfully accounts for well known psychophysical
effects for both static and dynamic contexts. By further considering inverse problem
techniques we define induction-reversed images: given a target image, we build an
image whose perceived luminance matches the actual luminance of the original
stimulus, thus effectively canceling out brightness induction effects. We suggest that
induction-reversed images may help remove undesired perceptual effects and can
find potential applications in fields such as radiological image interpretation |
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CIC |
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no |
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Admin @ si @ PDO2012a |
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2180 |
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Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu |
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A Neurodynamical Model Of Brightness Induction In V1 Following Static And Dynamic Contextual Influences |
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2012 |
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8th Federation of European Neurosciences |
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6 |
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63-64 |
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Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although striate cortex is traditionally regarded as an area mostly responsive to ensory (i.e. retinal) information,
neurophysiological evidence suggests that perceived brightness information mightbe explicitly represented in V1.
Such evidence has been observed both in anesthetised cats where neuronal response modulations have been found to follow luminance changes outside the receptive felds and in human fMRI measurements. In this work, possible neural mechanisms that ofer a plausible explanation for such phenomenon are investigated. To this end, we consider the model proposed by Z.Li (Li, Network:Comput. Neural Syst., 10 (1999)) which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual infuences, i.e. layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has reproduced other phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant efect of contextual infuences. We have extended the original model such that the input to the network is obtained from a complete multiscale and multiorientation wavelet decomposition, thereby allowing the recovery of an image refecting the perceived intensity. The proposed model successfully accounts for well known psychophysical efects for static contexts (among them: the White's and modifed White's efects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction efects) and also for brigthness induction in dynamic contexts defned by modulating the luminance of surrounding areas (e.g. the brightness of a static central area is perceived to vary in antiphase to the sinusoidal luminance changes of its surroundings). This work thus suggests that intra-cortical interactions in V1 could partially explain perceptual brightness induction efects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual processing pathway. |
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Admin @ si @ PDO2012b |
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2181 |
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Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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Title |
Predicting categorical colour perception in successive colour constancy |
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2012 |
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Perception |
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PER |
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41 |
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138 |
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Colour constancy is a perceptual mechanism that seeks to keep the colour of objects relatively stable under an illumination shift. Experiments haveshown that its effects depend on the number of colours present in the scene. We
studied categorical colour changes under different adaptation states, in particular, whether the colour categories seen under a chromatically neutral illuminant are the same after a shift in the chromaticity of the illumination. To do this, we developed the chromatic setting paradigm (2011 Journal of Vision11 349), which is as an extension of achromatic setting to colour categories. The paradigm exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. Our experiments were run on a CRT monitor (inside a dark room) under various simulated illuminants and restricting the number of colours of the Mondrian background to three, thus weakening the adaptation effect. Our results show a change in the colour categories present before (under neutral illumination) and after adaptation (under coloured illuminants) with a tendency for adapted colours to be less saturated than before adaptation. This behaviour was predicted by a simple
affine matrix model, adjusted to the chromatic setting results. |
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0301-0066 |
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Admin @ si @ RPV2012 |
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2188 |
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Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu |
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Which tone-mapping is the best? A comparative study of tone-mapping perceived quality |
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2014 |
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Perception |
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43 |
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106 |
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Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality
of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm
was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings. |
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CIC; NEUROBIT; 600.074 |
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Admin @ si @ CPO2014 |
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2527 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Colour Visual Coding in trained Deep Neural Networks |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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Admin @ si @ RaV2016b |
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2895 |
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