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| Author | F. Lopez; J.M. Valiente; Ramon Baldrich; Maria Vanrell |
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Fast surface grading using color statistics in the CIELab space | Type | Book Chapter | |||
| Year | 2005 | Publication | LNCS 1: 666–673 | Abbreviated Journal | ||
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| Address | Germany | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ LVB2005 | Serial | 641 | |||
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| Author | Robert Benavente; Maria Vanrell |
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| Title |
Fuzzy Colour Naming Based on Sigmoid Membership Functions. | Type | Miscellaneous | |||
| Year | 2004 | Publication | CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 | Abbreviated Journal | ||
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| Address | Aachen (Germany) | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ BeV2004 | Serial | 441 | |||
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| Author | Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Gaze control in a binocular robot systems | Type | Miscellaneous | |||
| Year | 1999 | Publication | Abbreviated Journal | |||
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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 | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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| Title |
High-Level Clothes Description Based on Color-Texture and Structural Features | Type | Book Chapter | |||
| Year | 2003 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | ||
| Volume | 2652 | Issue | Pages | 108–116 | ||
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| Abstract | This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. | |||||
| Address | Springer-Verlag | |||||
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| Notes | DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BTL2003a | Serial | 368 | |||
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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 | Susana Alvarez; Xavier Otazu; Maria Vanrell |
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| Title |
Image Segmentation Based on Inter-Feature Distance Maps | Type | Book Chapter | |||
| Year | 2005 | Publication | Frontiers in Artificial Intelligence and Applications, IOS Press, 131: 75–82 | Abbreviated Journal | ||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ AOV2005 | Serial | 569 | |||
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| Author | Maria Vanrell; Ramon Baldrich; Anna Salvatella; Robert Benavente; Francesc Tous |
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| Title |
Induction operators for a computational colour-texture representation | Type | Journal | |||
| Year | 2004 | Publication | Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651) | Abbreviated Journal | ||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ VBS2004 | Serial | 453 | |||
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| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
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| Title |
Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | |||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | ||
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| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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| Address | Virtual; September 2020 | |||||
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| Area | Expedition | Conference | BMVC | |||
| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | |||
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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 | Robert Benavente; C. Alejandro Parraga; Maria Vanrell |
 
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| Title |
La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas | Type | Conference Article | |||
| Year | 2010 | Publication | 9th Congreso Nacional del Color | Abbreviated Journal | ||
| Volume | Issue | Pages | 92–95 | |||
| Keywords | Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos | |||||
| Abstract | En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto. | |||||
| Address | Alicante (Spain) | |||||
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| ISSN | ISBN | 978-84-9717-144-1 | Medium | |||
| Area | Expedition | Conference | CNC | |||
| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ BPV2010 | Serial | 1327 | |||
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| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
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| Title |
Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | |||
| Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | ||
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| Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | |||||
| Address | Virtual; September 2020 | |||||
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| Area | Expedition | Conference | LIM | |||
| Notes | CIC; 600.118; 600.140; | Approved | no | |||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | |||
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| Author | Jaime Moreno; Xavier Otazu; Maria Vanrell |
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| Title |
Local Perceptual Weighting in JPEG2000 for Color Images | Type | Conference Article | |||
| Year | 2010 | Publication | 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science | Abbreviated Journal | ||
| Volume | Issue | Pages | 255–260 | |||
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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 (Chromatic Induction Wavelet Model). | |||||
| Address | Joensuu, Finland | |||||
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| ISSN | ISBN | 9781617388897 | Medium | |||
| Area | Expedition | Conference | CGIV/MCS | |||
| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ MOV2010a | Serial | 1307 | |||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
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| Title |
Low-dimensional and Comprehensive Color Texture Description | Type | Journal Article | |||
| Year | 2012 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU | |
| Volume | 116 | Issue | I | Pages | 54-67 | |
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| Abstract | Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap |
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| ISSN | 1077-3142 | ISBN | Medium | |||
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| Notes | CAT;CIC | Approved | no | |||
| Call Number | Admin @ si @ ASV2012 | Serial | 1827 | |||
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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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| 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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