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| Author | C. Alejandro Parraga |
 
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| Title | Perceptual Psychophysics | Type | Book Chapter | |||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | ||
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| Publisher | Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | |||
| Language | Summary Language | Original Title | ||||
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| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | 978-3-527-41264-8 | Medium | |||
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CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | |||
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| Author | Marc Serra |
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| Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | |||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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| Address | September 2015 | |||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Robert Benavente;Olivier Penacchio | ||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | 978-84-943427-4-5 | Medium | |||
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| Notes |
CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Ser2015 | Serial | 2688 | |||
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| Author | A.Gonzalez; 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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| Series Volume | Series Issue | Edition | ||||
| ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | ||
| Area | Expedition | Conference | ||||
| Notes |
CIC; 600.052; 605.203 | Approved | no | |||
| Call Number | Admin @ si @ GBP2013 | Serial | 2266 | |||
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| Author | Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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| Title | Chromatic settings and the structural color constancy index | Type | Journal Article | |||
| Year | 2013 | Publication | Journal of Vision | Abbreviated Journal | JV | |
| Volume | 13 | Issue | 4-3 | Pages | 1-26 | |
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| Abstract | Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects. | |||||
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| Notes |
CIC; 600.052; 600.051; 605.203 | Approved | no | |||
| Call Number | Admin @ si @ RPV2013 | Serial | 2288 | |||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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| Title | The Photometry of Intrinsic Images | Type | Conference Article | |||
| Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 1494-1501 | |||
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| Abstract | Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. | |||||
| Address | Columbus; Ohio; USA; June 2014 | |||||
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| Area | Expedition | Conference | CVPR | |||
| Notes |
CIC; 600.052; 600.051; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ SPB2014 | Serial | 2506 | |||
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| Author | Ivet Rafegas; Maria Vanrell |
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| Title | Color encoding in biologically-inspired convolutional neural networks | Type | Journal Article | |||
| Year | 2018 | Publication | Vision Research | Abbreviated Journal | VR | |
| Volume | 151 | Issue | Pages | 7-17 | ||
| Keywords | Color coding; Computer vision; Deep learning; Convolutional neural networks | |||||
| Abstract | 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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| Notes |
CIC; 600.051; 600.087 | Approved | no | |||
| Call Number | Admin @ si @RaV2018 | Serial | 3114 | |||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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| Title | Low-level SpatioChromatic Grouping for Saliency Estimation | Type | Journal Article | |||
| Year | 2013 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI | |
| Volume | 35 | Issue | 11 | Pages | 2810-2816 | |
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| Abstract | 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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| Series Volume | Series Issue | Edition | ||||
| ISSN | 0162-8828 | ISBN | Medium | |||
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| Notes |
CIC; 600.051; 600.052; 605.203 | Approved | no | |||
| Call Number | Admin @ si @ MVO2013 | Serial | 2289 | |||
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| Author | Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras |
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| Title | Intrinsic Image Evaluation On Synthetic Complex Scenes | Type | Conference Article | |||
| Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | ||
| Volume | Issue | Pages | 285 - 289 | |||
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| Abstract | Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes. |
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| Address | Melbourne; Australia; September 2013 | |||||
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| Area | Expedition | Conference | ICIP | |||
| Notes |
CIC; 600.048; 600.052; 600.051 | Approved | no | |||
| Call Number | Admin @ si @ BSW2013 | Serial | 2264 | |||
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| Author | Joost Van de Weijer; Fahad Shahbaz Khan |
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| Title | Fusing Color and Shape for Bag-of-Words Based Object Recognition | Type | Conference Article | |||
| Year | 2013 | Publication | 4th Computational Color Imaging Workshop | Abbreviated Journal | ||
| Volume | 7786 | Issue | Pages | 25-34 | ||
| Keywords | Object Recognition; color features; bag-of-words; image classification | |||||
| Abstract | In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. | |||||
| Address | Chiba; Japan; March 2013 | |||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | |||
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| ISSN | 0302-9743 | ISBN | 978-3-642-36699-4 | Medium | ||
| Area | Expedition | Conference | CCIW | |||
| Notes |
CIC; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ WeK2013 | Serial | 2283 | |||
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| Author | Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat |
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| Title | Discriminative Color Descriptors | Type | Conference Article | |||
| Year | 2013 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 2866 - 2873 | |||
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| Abstract | Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200. | |||||
| Address | Portland; Oregon; June 2013 | |||||
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| Series Volume | Series Issue | Edition | ||||
| ISSN | 1063-6919 | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | |||
| Notes |
CIC; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ KWK2013a | Serial | 2262 | |||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Sadiq Ali; Michael Felsberg |
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| Title | Evaluating the impact of color on texture recognition | Type | Conference Article | |||
| Year | 2013 | Publication | 15th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | ||
| Volume | 8047 | Issue | Pages | 154-162 | ||
| Keywords | Color; Texture; image representation | |||||
| Abstract | State-of-the-art texture descriptors typically operate on grey scale images while ignoring color information. A common way to obtain a joint color-texture representation is to combine the two visual cues at the pixel level. However, such an approach provides sub-optimal results for texture categorisation task.
In this paper we investigate how to optimally exploit color information for texture recognition. We evaluate a variety of color descriptors, popular in image classification, for texture categorisation. In addition we analyze different fusion approaches to combine color and texture cues. Experiments are conducted on the challenging scenes and 10 class texture datasets. Our experiments clearly suggest that in all cases color names provide the best performance. Late fusion is the best strategy to combine color and texture. By selecting the best color descriptor with optimal fusion strategy provides a gain of 5% to 8% compared to texture alone on scenes and texture datasets. |
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| Address | York; UK; August 2013 | |||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | |||
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| ISSN | 0302-9743 | ISBN | 978-3-642-40260-9 | Medium | ||
| Area | Expedition | Conference | CAIP | |||
| Notes |
CIC; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ KWA2013 | Serial | 2263 | |||
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| Author | Hassan Ahmed Sial |
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| Title | Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation | Type | Book Whole | |||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | In this thesis, we explore how to estimate the effects of the light interacting with the scene objects from a single image. To achieve this goal, we focus on recovering intrinsic components like reflectance, shading, or light properties such as color and position using deep architectures. The success of these approaches relies on training on large and diversified image datasets. Therefore, we present several contributions on this such as: (a) a data-augmentation technique; (b) a ground-truth for an existing multi-illuminant dataset; (c) a family of synthetic datasets, SID for Surreal Intrinsic Datasets, with diversified backgrounds and coherent light conditions; and (d) a practical pipeline to create hybrid ground-truths to overcome the complexity of acquiring realistic light conditions in a massive way. In parallel with the creation of datasets, we trained different flexible encoder-decoder deep architectures incorporating physical constraints from the image formation models.
In the last part of the thesis, we apply all the previous experience to two different problems. Firstly, we create a large hybrid Doc3DShade dataset with real shading and synthetic reflectance under complex illumination conditions, that is used to train a two-stage architecture that improves the character recognition task in complex lighting conditions of unwrapped documents. Secondly, we tackle the problem of single image scene relighting by extending both, the SID dataset to present stronger shading and shadows effects, and the deep architectures to use intrinsic components to estimate new relit images. |
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| Address | September 2021 | |||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | IMPRIMA | Place of Publication | Editor | Maria Vanrell;Ramon Baldrich | ||
| Language | Summary Language | Original Title | ||||
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| ISSN | ISBN | 978-84-122714-8-5 | Medium | |||
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CIC; | Approved | no | |||
| Call Number | Admin @ si @ Sia2021 | Serial | 3607 | |||
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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 | Miscellaneous | |||
| Year | 1999 | Publication | Abbreviated Journal | |||
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| Address | Girona | |||||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ BOV1999 | Serial | 47 | |||
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| Author | Robert Benavente |
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| Title | Dealing with colour variability: application to a colour naming task | Type | Report | |||
| Year | 1999 | Publication | CVC Technical Report #32 | Abbreviated Journal | ||
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| Address | CVC (UAB) | |||||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ Ben1999 | Serial | 53 | |||
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| Author | Ramon Baldrich |
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| Title | Perceptual approach to a computational colour-texture representation for surface inspection. | Type | Book Whole | |||
| Year | 2001 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ Bal2001 | Serial | 73 | |||
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| Author | Robert Benavente; Maria Vanrell |
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| Title | A colour naming experiment | Type | Report | |||
| Year | 2001 | Publication | CVC Technical Report #56 | Abbreviated Journal | ||
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| Address | CVC (UAB) | |||||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ BeV2001 | Serial | 78 | |||
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| Author | O. Fors; Xavier Otazu; J. Nuñez |
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| Title | Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT. | Type | Miscellaneous | |||
| Year | 2001 | Publication | Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 546–550. | Abbreviated Journal | ||
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| Address | Lleida | |||||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ FON2001 | Serial | 94 | |||
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| Author | Xavier Otazu; J. Nuñez |
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| Title | Algoritmo de Clasificacion no Supervisada Basado en Wavelets. | Type | Miscellaneous | |||
| Year | 2001 | Publication | Teledeteccion, Medio Ambiente y Cambio Global, IX Congreso Nacional de Teledeteccion, 437–440. | Abbreviated Journal | ||
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| Address | Lleida | |||||
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CIC | Approved | no | |||
| Call Number | CAT @ cat @ ONu2001 | Serial | 147 | |||
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