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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 |
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Conference Article |
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Year |
2013 |
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20th IEEE International Conference on Image Processing |
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285 - 289 |
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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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Melbourne; Australia; September 2013 |
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CIC; 600.048; 600.052; 600.051 |
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Admin @ si @ BSW2013 |
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2264 |
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Author |
Shida Beigpour; Joost Van de Weijer |
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Title |
Photo-Realistic Color Alteration for Architecture and Design |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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84–88 |
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As color is a strong stimuli we receive from the exterior world, choosing the right color can prove crucial in creating the desired architecture and desing. We propose a framework to apply a realistic color change on both objects and their illuminant lights for snapshots of architectural designs, in order to visualize and choose the right color before actully applying the change in the real world. The proposed framework is based on the laws of physics in order to accomplish realistic and physically plausible results. |
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Gjovik (Norway) |
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CREATE |
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CAT @ cat @ BeW2010 |
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1330 |
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Author |
Shida Beigpour; Joost Van de Weijer |
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Title |
Object Recoloring Based on Intrinsic Image Estimation |
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2011 |
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13th IEEE International Conference in Computer Vision |
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327 - 334 |
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Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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Admin @ si @ BeW2011 |
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1781 |
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Author |
Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou |
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Title |
Multi-Illuminant Estimation with Conditional Random Fields |
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2014 |
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IEEE Transactions on Image Processing |
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TIP |
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23 |
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1 |
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83-95 |
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color constancy; CRF; multi-illuminant |
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Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. |
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1057-7149 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ BRW2014 |
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2451 |
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Author |
Shida Beigpour |
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Title |
Illumination and object reflectance modeling |
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2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information. |
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Barcelona |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Joost Van de Weijer;Ernest Valveny |
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Admin @ si @ Bei2013 |
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2267 |
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Author |
Shida Beigpour |
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Title |
Physics-based Reflectance Estimation Applied to Recoloring |
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Report |
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2009 |
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CVC Technical Report |
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137 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Bei2009 |
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2396 |
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Author |
Sandra Jimenez; Xavier Otazu; Valero Laparra; Jesus Malo |
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Title |
Chromatic induction and contrast masking: similar models, different goals? |
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Conference Article |
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2013 |
Publication |
Human Vision and Electronic Imaging XVIII |
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8651 |
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Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
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San Francisco CA; USA; February 2013 |
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HVEI |
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Admin @ si @ JOL2013 |
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2240 |
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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 |
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Conference Article |
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2020 |
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31st British Machine Vision Conference |
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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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Virtual; September 2020 |
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BMVC |
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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ DSM2020 |
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3461 |
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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. |
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Miscellaneous |
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2000 |
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Computacion y Sistemas, 4(1):30–43. |
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242 |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Title |
Estimation of Fuzzy Sets for Computational Colour Categorization |
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2004 |
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Color Research and Application, 29(5):342–353 (IF: 0.739) |
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484 |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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A data set for fuzzy colour naming |
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2006 |
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Color Research & Application, 31(1):48–56 |
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590 |
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Robert Benavente; Maria Vanrell; Ramon Baldrich |
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Parametric Fuzzy Sets for Automatic Color Naming |
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2008 |
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Journal of the Optical Society of America A |
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2582–2593 |
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1004 |
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Robert Benavente; Maria Vanrell |
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A colour naming experiment |
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2001 |
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CVC Technical Report #56 |
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78 |
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Robert Benavente; Maria Vanrell |
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Fuzzy Colour Naming Based on Sigmoid Membership Functions. |
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2004 |
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CGIV 2004 Second European Conference on Colour in Graphics, Imaging and Vision, 135:139 |
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Aachen (Germany) |
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441 |
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Robert Benavente; Maria Vanrell |
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Title |
Parametrizacion del Espacio de Categorias de Color |
Type |
Miscellaneous |
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Year |
2007 |
Publication |
Proceedings del VIII Congreso Nacional del Color |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
77–78 |
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Address |
Madrid (Spain) |
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Conference |
CNC’07 |
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Notes |
CAT;CIC |
Approved |
no |
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Call Number |
CAT @ cat @ BeV2007 |
Serial |
905 |
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| Permanent link to this record |
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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 |
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Year |
1999 |
Publication |
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Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Abstract |
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Address |
Girona |
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CIC |
Approved |
no |
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Call Number |
CAT @ cat @ BOV1999 |
Serial |
47 |
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| Permanent link to this record |
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Author |
Robert Benavente; Laura Igual; Fernando Vilariño |
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Title |
Current Challenges in Computer Vision |
Type |
Book Whole |
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Year |
2008 |
Publication |
Proccedings of the Third Internal Workshop |
Abbreviated Journal |
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Issue |
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ISBN |
978-84-936529-0-6 |
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Expedition |
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Conference |
CVCRD |
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Notes |
MILAB;CIC;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ BIV2008 |
Serial |
1110 |
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| Permanent link to this record |
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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 |
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Year |
2000 |
Publication |
15 th International Conference on Pattern Recognition, 3:637–641. |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Address |
Barcelona. |
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Expedition |
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Conference |
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Notes |
DAG;CIC |
Approved |
no |
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Call Number |
CAT @ cat @ BSB2000 |
Serial |
223 |
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| Permanent link to this record |
