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| Author | Xavier Otazu; Maria Vanrell; C. Alejandro Parraga | ||||
| Title | Multiresolution Wavelet Framework Models Brightness Induction Effects | Type | Journal | ||
| Year | 2008 | Publication | Vision Research | Abbreviated Journal | VR |
| Volume | 48 | Issue | 5 | Pages  |
733–751 |
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| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ OVP2008a | Serial | 927 | ||
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| Author | Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados | ||||
| Title | Normalized colour segmentation for human appearance description. | Type | Conference Article | ||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 3 | Issue | Pages |
637-641 | |
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| Address | Barcelona. | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG;CIC | Approved | no | ||
| Call Number | CAT @ cat @ BSB2000 | Serial | 223 | ||
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| Author | Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich | ||||
| Title | DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II | Abbreviated Journal | |
| Volume | 9475 | Issue | Pages |
463-473 | |
| Keywords | Projector-camera systems; Feature descriptors; Object recognition | ||||
| Abstract | Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-27862-9 | Medium | |
| Area | Expedition | Conference | ISVC | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ SMG2015 | Serial | 2736 | ||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga | ||||
| Title | Saliency Estimation Using a Non-Parametric Low-Level Vision Model | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
433-440 | ||
| Keywords | Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms | ||||
| Abstract | Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. | ||||
| Address | Colorado Springs | ||||
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| ISSN | 1063-6919 | ISBN | 978-1-4577-0394-2 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MVO2011 | Serial | 1757 | ||
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| Author | Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados | ||||
| Title | Textual Descriptions for Browsing People by Visual Apperance. | Type | Book Chapter | ||
| Year | 2002 | Publication | Lecture Notes in Artificial Intelligence | Abbreviated Journal | |
| Volume | 2504 | Issue | Pages |
419-429 | |
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| Abstract | This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building | ||||
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| Publisher | Springer Verlag | Place of Publication | Editor | ||
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| Notes | DAG;CIC | Approved | no | ||
| Call Number | CAT @ cat @ TBB2002b | Serial | 319 | ||
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| Author | Javier Vazquez; Maria Vanrell; Ramon Baldrich | ||||
| Title | Towards a Psychophysical Evaluation of Colour Constancy Algorithms | Type | Conference Article | ||
| Year | 2008 | Publication | 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings | Abbreviated Journal | |
| Volume | Issue | Pages |
372–377 | ||
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| Address | Terrassa (Spain) | ||||
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| Area | Expedition | Conference | CGIV08 | ||
| Notes | CAT;CIC | Approved | no | ||
| Call Number | CAT @ cat @ VVB2008a | Serial | 968 | ||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu | ||||
| Title | 3D Texton Spaces for color-texture retrieval | Type | Conference Article | ||
| Year | 2010 | Publication | 7th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 6111 | Issue | Pages |
354–363 | |
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| Abstract | Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | A.C. Campilho and M.S. Kamel | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-13771-6 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ ASV2010a | Serial | 1325 | ||
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| Author | Jordi Roca; Maria Vanrell; C. Alejandro Parraga | ||||
| Title | What is constant in colour constancy? | Type | Conference Article | ||
| Year | 2012 | Publication | 6th European Conference on Colour in Graphics, Imaging and Vision | Abbreviated Journal | |
| Volume | Issue | Pages |
337-343 | ||
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| Abstract | Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy algorithms [3] for estimating the scene illuminant. |
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| ISSN | ISBN | 9781622767014 | Medium | ||
| Area | Expedition | Conference | CGIV | ||
| Notes | CIC | Approved | no | ||
| Call Number | RVP2012 | Serial | 2189 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
| Title | Who Painted this Painting? | Type | Conference Article | ||
| Year | 2010 | Publication | Proceedings of The CREATE 2010 Conference | Abbreviated Journal | |
| Volume | Issue | Pages |
329–333 | ||
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| Address | Gjovik (Norway) | ||||
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| Area | Expedition | Conference | CREATE | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ KWV2010 | Serial | 1329 | ||
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| Author | Javier Vazquez; Maria Vanrell; Robert Benavente | ||||
| 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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| Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages |
318-325 | |
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| Abstract | 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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| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
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| Author | Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus | ||||
| 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 | Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras | ||||
| 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 | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell | ||||
| 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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