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| Author | Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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| Title | Gaze control in a binocular robot systems | Type | Miscellaneous | |||
| Year | 1999 | Publication  |
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| Address | Barcelona | |||||
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| Notes | OR;ISE;CIC;MV | Approved | no | |||
| Call Number | BCNPCL @ bcnpcl @ RVV1999b | Serial | 41 | |||
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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 |
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| Address | Girona | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ BOV1999 | Serial | 47 | |||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell |
 
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| Title | Top-Down Color Attention for Object Recognition | Type | Conference Article | |||
| Year | 2009 | Publication |
12th International Conference on Computer Vision | Abbreviated Journal | ||
| Volume | Issue | Pages | 979 - 986 | |||
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| Abstract | Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information. | |||||
| Address | Kyoto, Japan | |||||
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| ISSN | 1550-5499 | ISBN | 978-1-4244-4420-5 | Medium | ||
| Area | Expedition | Conference | ICCV | |||
| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ SWV2009 | Serial | 1196 | |||
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| Author | Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados |
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| Title | Normalized colour segmentation for human appearance description. | Type | Miscellaneous | |||
| Year | 2000 | Publication |
15 th International Conference on Pattern Recognition, 3:637–641. | Abbreviated Journal | ||
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| Address | Barcelona. | |||||
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| Notes | DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BSB2000 | Serial | 223 | |||
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| Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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| Title | High-Level Clothes Description Based on Colour-Texture and Structural Features | Type | Conference Article | |||
| Year | 2003 | Publication |
1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | ||
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| Address | Palma de Mallorca | |||||
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| Notes | DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BTL2003b | Serial | 369 | |||
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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 | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
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| Title | Perceptual color texture codebooks for retrieving in highly diverse texture datasets | Type | Conference Article | |||
| Year | 2010 | Publication |
20th International Conference on Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 866–869 | |||
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| Abstract | Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel. | |||||
| Address | Istanbul (Turkey) | |||||
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| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | ||
| Area | Expedition | Conference | ICPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ ASV2010b | Serial | 1426 | |||
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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 | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell |
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| Title | Portmanteau Vocabularies for Multi-Cue Image Representation | Type | Conference Article | |||
| Year | 2011 | Publication |
25th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | ||
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| Abstract | We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation | |||||
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| Area | Expedition | Conference | NIPS | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ KWB2011 | Serial | 1865 | |||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez |
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| Title | Color Attributes for Object Detection | Type | Conference Article | |||
| Year | 2012 | Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 3306-3313 | |||
| Keywords | pedestrian detection | |||||
| Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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| Address | Providence; Rhode Island; USA; | |||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | |||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | ADAS; CIC; | Approved | no | |||
| Call Number | Admin @ si @ KRW2012 | Serial | 1935 | |||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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| Title | Names and Shades of Color for Intrinsic Image Estimation | Type | Conference Article | |||
| Year | 2012 | Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 278-285 | |||
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| Abstract | In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. | |||||
| Address | Providence, Rhode Island | |||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | |||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ SPB2012 | Serial | 2026 | |||
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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 | 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 | Xavier Otazu; Maria Vanrell |
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| Title | Several lightness induction effects with a computational multiresolution wavelet framework | Type | Journal | |||
| Year | 2006 | Publication |
29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 | Abbreviated Journal | ||
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| Address | Saint-Petersburg (Russia) | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ OtV2006 | Serial | 659 | |||
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