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| Author |
Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez |
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| Title |
Enhancing spatio-chromatic representation with more-than-three color coding for image description |
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| Year |
2017 |
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Journal of the Optical Society of America A |
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JOSA A |
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| Volume |
34 |
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5 |
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827-837 |
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Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. |
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CIC; 600.087 |
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no |
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Admin @ si @ RVB2017 |
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2892 |
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| Author |
Ivet Rafegas; Maria Vanrell |
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| Title |
Color encoding in biologically-inspired convolutional neural networks |
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2018 |
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Vision Research |
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VR |
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| Volume |
151 |
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7-17 |
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Color coding; Computer vision; Deep learning; Convolutional neural networks |
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Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. |
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CIC; 600.051; 600.087 |
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Admin @ si @RaV2018 |
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3114 |
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Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
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| Title |
Understanding trained CNNs by indexing neuron selectivity |
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2020 |
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Pattern Recognition Letters |
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PRL |
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136 |
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318-325 |
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The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. |
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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ RVL2019 |
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3310 |
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Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
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Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects |
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2020 |
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Journal of the Optical Society of America A |
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JOSA A |
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37 |
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1 |
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1-15 |
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Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. |
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CIC; 600.140; 600.12; 600.118 |
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Admin @ si @ SBV2019 |
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3311 |
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Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr |
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Colour-emotion associations in individuals with red-green colour blindness |
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2021 |
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PeerJ |
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9 |
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e11180 |
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Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. |
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Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. |
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CIC; LAMP; 600.120; 600.128 |
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Admin @ si @ JCP2021 |
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3564 |
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| Author |
Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
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Matching visual induction effects on screens of different size |
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2021 |
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Journal of Vision |
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JOV |
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21 |
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6(10) |
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1-22 |
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In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. |
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CIC |
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Admin @ si @ CVM2021 |
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3595 |
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Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown |
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| Title |
Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring |
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2023 |
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Computer Graphics Forum |
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CGF |
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Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. |
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CIC; MACO |
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Admin @ si @ XVH2023 |
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3883 |
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Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray |
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A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions |
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2023 |
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Journal of Vision |
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JV |
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23 |
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9 |
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4817-4817 |
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A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception. |
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MACO; CIC |
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Admin @ si @ PFV2023 |
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3890 |
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Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown |
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Noise Prism: A Novel Multispectral Visualization Technique |
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2021 |
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31st Color and Imaging Conference |
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A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique. |
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MACO; CIC |
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Admin @ si @ CVL2021 |
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4000 |
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Xavier Otazu; M. Ribo; M. Peracaula; J.M. Paredes; J. Nuñez |
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Detection of superimposed periodic signals using wavelets |
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2002 |
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Monthly Notices of the Royal Astronomical Society, 333, 2: 365–372 (IF: 4.671) |
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Xavier Otazu; M. Ribo; J.M. Paredes; M. Peracaula; J. Nuñez |
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Multiresolution approach for period determination on unevenly sampled data |
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Monthly Notices of the Royal Astronomical Society, 351:251–219 (IF: 5.238) |
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Maria Vanrell; Ramon Baldrich; Anna Salvatella; Robert Benavente; Francesc Tous |
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Induction operators for a computational colour-texture representation |
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2004 |
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Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651) |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Estimation of Fuzzy Sets for Computational Colour Categorization |
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2004 |
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Color Research and Application, 29(5):342–353 (IF: 0.739) |
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484 |
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M. Gonzalez-Audicana; Xavier Otazu; O. Fors; A. Seco |
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Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images |
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International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925) |
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CIC |
Approved |
no |
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| Call Number |
CAT @ cat @ GOF2005 |
Serial |
530 |
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| Author |
Xavier Otazu; Maria Vanrell |
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| Title |
Perceptual representation of textured images |
Type |
Journal |
| Year |
2005 |
Publication |
Journal of Imaging Science and Technology, 49(3):262–271 (IF: 0.522) |
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| Notes |
CIC |
Approved |
no |
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| Call Number |
CAT @ cat @ OtV2005b |
Serial |
542 |
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| Author |
Maria Vanrell; Jordi Vitria |
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| Title |
Optimal 3x3 decomposable disks for morphological transformations |
Type |
Journal |
| Year |
1997 |
Publication |
Image and Vision Computing, 15(2): 845–854 |
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| Notes |
OR;CIC;MV |
Approved |
no |
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| Call Number |
BCNPCL @ bcnpcl @ VaV1997c |
Serial |
543 |
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| Author |
Xavier Otazu; M. Gonzalez-Audicana; O. Fors; J. Nuñez |
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| Title |
Introduction of Sensor Spectral Response Into Image Fusion Methods. Application to Wavelet-Based Methods |
Type |
Journal |
| Year |
2005 |
Publication |
IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376–2385 (IF: 1.627) |
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| Notes |
CIC |
Approved |
no |
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| Call Number |
CAT @ cat @ OGF2005 |
Serial |
564 |
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| |
| Author |
A. Richichi; O. Fors; M.T. Merino; Xavier Otazu; J. Nuñez; A. Prades; U. Thiele; D. Perez-Ramirez; F.J. Montojo |
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| Title |
The Calar Alto lunar occultation program: update and new results |
Type |
Journal |
| Year |
2006 |
Publication |
Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088 |
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| Notes |
CIC |
Approved |
no |
|
| Call Number |
CAT @ cat @ RFM2006a |
Serial |
589 |
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| Permanent link to this record |
