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Author | Jaime Moreno; Xavier Otazu; Maria Vanrell |
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Title | Contribution of CIWaM in JPEG2000 Quantization for Color Images | Type | Conference Article | |||
Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 132–136 | |||
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Abstract | The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM(ChromaticInductionWaveletModel). | |||||
Address | Gjovik (Norway) | |||||
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Area | Expedition | Conference | CREATE | |||
Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ MOV2010b | Serial | 1308 | |||
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Author | Ivet Rafegas; Maria Vanrell |
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Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | |||
Year | 2016 | Publication | European Conference on Visual Perception | Abbreviated Journal | ||
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Address | Barcelona; Spain; August 2016 | |||||
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Area | Expedition | Conference | ECVP | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RaV2016b | Serial | 2895 | |||
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Author | Robert Benavente; M.C. Olive; Maria Vanrell; Ramon Baldrich |
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Title | Colour Perception: A Simple Method for Colour Naming. | Type | Conference Article | |||
Year | 1999 | Publication | Proc. 2nd Catalan Congress on Artificial Intelligence (CCIA’99 | Abbreviated Journal | ||
Volume | Issue | Pages | 340-347 | |||
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Address | Girona | |||||
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Area | Expedition | Conference | CCIA | |||
Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ BOV1999 | Serial | 47 | |||
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Author | Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title | Colour Normalisation Based on Background Information. | Type | Miscellaneous | |||
Year | 2001 | Publication | Proceeding ICIP 2001, IEEE International Conference on Image Processing | Abbreviated Journal | ICIP 2001 | |
Volume | Issue | 1 | Pages | 874–877 | ||
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Address | Grecia. | |||||
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Notes | ADAS;DAG;CIC | Approved | no | |||
Call Number | ADAS @ adas @ VLP2001 | Serial | 167 | |||
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Author | Robert Benavente; Ramon Baldrich; M.C. Olive; Maria Vanrell |
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Title | Colour Naming Considering the Colour Variability Problem. | Type | Miscellaneous | |||
Year | 2000 | Publication | Computacion y Sistemas, 4(1):30–43. | Abbreviated Journal | ||
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Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ BBO2000 | Serial | 242 | |||
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Author | Xavier Otazu; Maria Vanrell; C. Alejandro Parraga |
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Title | Colour induction effects are modelled by a low-level multiresolution wavelet framework | Type | Journal | |||
Year | 2008 | Publication | Perception 37(Suppl.): 107 | Abbreviated Journal | ||
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Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ OVP2008b | Serial | 1055 | |||
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Author | Robert Benavente; C. Alejandro Parraga; Maria Vanrell |
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Title | Colour categories boundaries are better defined in contextual conditions | Type | Journal Article | |||
Year | 2009 | Publication | Perception | Abbreviated Journal | PER | |
Volume | 38 | Issue | Pages | 36 | ||
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Abstract | In a previous experiment [Parraga et al, 2009 Journal of Imaging Science and Technology 53(3)] the boundaries between basic colour categories were measured by asking subjects to categorize colour samples presented in isolation (ie on a dark background) using a YES/NO paradigm. Results showed that some boundaries (eg green – blue) were very diffuse and the subjects' answers presented bimodal distributions, which were attributed to the emergence of non-basic categories in those regions (eg turquoise). To confirm these results we performed a new experiment focussed on the boundaries where bimodal distributions were more evident. In this new experiment rectangular colour samples were presented surrounded by random colour patches to simulate contextual conditions on a calibrated CRT monitor. The names of two neighbouring colours were shown at the bottom of the screen and subjects selected the boundary between these colours by controlling the chromaticity of the central patch, sliding it across these categories' frontier. Results show that in this new experimental paradigm, the formerly uncertain inter-colour category boundaries are better defined and the dispersions (ie the bimodal distributions) that occurred in the previous experiment disappear. These results may provide further support to Berlin and Kay's basic colour terms theory. | |||||
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Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ BPV2009 | Serial | 1192 | |||
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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 | Type | Book Chapter | |||
Year | 2013 | Publication | Multimodal Interaction in Image and Video Applications | Abbreviated Journal | ||
Volume | 48 | Issue | Pages | 47-66 | ||
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Abstract | 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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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | |||
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ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | ||
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Notes | CIC; 600.052; 605.203 | Approved | no | |||
Call Number | Admin @ si @ GBP2013 | Serial | 2266 | |||
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Author | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
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Title | Color-based data augmentation for Reflectance Estimation | Type | Conference Article | |||
Year | 2018 | Publication | 26th Color Imaging Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 284-289 | |||
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Abstract | Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. | |||||
Address | Vancouver; November 2018 | |||||
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Area | Expedition | Conference | CIC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ SSB2018a | Serial | 3129 | |||
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Author | Felipe Lumbreras; Joan Serrat; Ramon Baldrich; Maria Vanrell; Juan J. Villanueva |
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Title | Color Texture Recognition Through Multiresolution Features | Type | Miscellaneous | |||
Year | 2001 | Publication | QCAV 2001 International Conference on Quality Control by Artificial Vision, France, 1:114–121. | Abbreviated Journal | ||
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Notes | ADAS;CIC | Approved | no | |||
Call Number | ADAS @ adas @ LSB2001 | Serial | 124 | |||
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Author | Ivet Rafegas; Maria Vanrell |
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Title | Color spaces emerging from deep convolutional networks | Type | Conference Article | |||
Year | 2016 | Publication | 24th Color and Imaging Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 225-230 | |||
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Abstract | Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
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Address | San Diego; USA; November 2016 | |||||
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Area | Expedition | Conference | CIC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RaV2016a | Serial | 2894 | |||
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Author | Ivet Rafegas; Maria Vanrell |
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Title | Color representation in CNNs: parallelisms with biological vision | Type | Conference Article | |||
Year | 2017 | Publication | ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision | Abbreviated Journal | ||
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Abstract | Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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Address | Venice; Italy; October 2017 | |||||
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Area | Expedition | Conference | ICCV-MBCC | |||
Notes | CIC; 600.087; 600.051 | Approved | no | |||
Call Number | Admin @ si @ RaV2017 | Serial | 2984 | |||
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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 | Type | Book Chapter | |||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | ||
Volume | Issue | 17 | Pages | 287-317 | ||
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Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | ||
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ WBV2012 | Serial | 2063 | |||
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Author | Javier Vazquez; Maria Vanrell; Robert Benavente |
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Title | Color names as a constraint for Computer Vision problems | Type | Conference Article | |||
Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | ||
Volume | Issue | Pages | 324–328 | |||
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Abstract | Computer Vision Problems are usually ill-posed. Constraining de gamut of possible solutions is then a necessary step. Many constrains for different problems have been developed during years. In this paper, we present a different way of constraining some of these problems: the use of color names. In particular, we will focus on segmentation, representation ans constancy. | |||||
Address | Gjovik (Norway) | |||||
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Area | Expedition | Conference | CREATE | |||
Notes | CIC | Approved | no | |||
Call Number | CAT @ cat @ VVB2010 | Serial | 1328 | |||
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