Susana Alvarez, & Maria Vanrell. (2012). Texton theory revisited: a bag-of-words approach to combine textons. PR - Pattern Recognition, 45(12), 4312–4325.
Abstract: The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.
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C. Alejandro Parraga, Jordi Roca, & Maria Vanrell. (2011). Do Basic Colors Influence Chromatic Adaptation? VSS - Journal of Vision, 11(11), 85.
Abstract: Color constancy (the ability to perceive colors relatively stable under different illuminants) is the result of several mechanisms spread across different neural levels and responding to several visual scene cues. It is usually measured by estimating the perceived color of a grey patch under an illuminant change. In this work, we hypothesize whether chromatic adaptation (without a reference white or grey) could be driven by certain colors, specifically those corresponding to the universal color terms proposed by Berlin and Kay (1969). To this end we have developed a new psychophysical paradigm in which subjects adjust the color of a test patch (in CIELab space) to match their memory of the best example of a given color chosen from the universal terms list (grey, red, green, blue, yellow, purple, pink, orange and brown). The test patch is embedded inside a Mondrian image and presented on a calibrated CRT screen inside a dark cabin. All subjects were trained to “recall” their most exemplary colors reliably from memory and asked to always produce the same basic colors when required under several adaptation conditions. These include achromatic and colored Mondrian backgrounds, under a simulated D65 illuminant and several colored illuminants. A set of basic colors were measured for each subject under neutral conditions (achromatic background and D65 illuminant) and used as “reference” for the rest of the experiment. The colors adjusted by the subjects in each adaptation condition were compared to the reference colors under the corresponding illuminant and a “constancy index” was obtained for each of them. Our results show that for some colors the constancy index was better than for grey. The set of best adapted colors in each condition were common to a majority of subjects and were dependent on the chromaticity of the illuminant and the chromatic background considered.
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Naila Murray, Maria Vanrell, Xavier Otazu, & C. Alejandro Parraga. (2013). Low-level SpatioChromatic Grouping for Saliency Estimation. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2810–2816.
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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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2008). Parametric Fuzzy Sets for Automatic Color Naming. Journal of the Optical Society of America A, 2582–2593.
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Maria Vanrell, Felipe Lumbreras, A. Pujol, Ramon Baldrich, Josep Llados, & Juan J. Villanueva. (2001). Colour Normalisation Based on Background Information..
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Xavier Boix, Josep M. Gonfaus, Joost Van de Weijer, Andrew Bagdanov, Joan Serrat, & Jordi Gonzalez. (2012). Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation. IJCV - International Journal of Computer Vision, 96(1), 83–102.
Abstract: The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
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Fahad Shahbaz Khan, Joost Van de Weijer, & Maria Vanrell. (2012). Modulating Shape Features by Color Attention for Object Recognition. IJCV - International Journal of Computer Vision, 98(1), 49–64.
Abstract: Bag-of-words based image representation is a successful approach for object recognition. Generally, 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, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.
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Shida Beigpour, Christian Riess, Joost Van de Weijer, & Elli Angelopoulou. (2014). Multi-Illuminant Estimation with Conditional Random Fields. TIP - IEEE Transactions on Image Processing, 23(1), 83–95.
Abstract: 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.
Keywords: color constancy; CRF; multi-illuminant
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Hassan Ahmed Sial, Ramon Baldrich, & Maria Vanrell. (2020). Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects. JOSA A - Journal of the Optical Society of America A, 37(1), 1–15.
Abstract: Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
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Felipe Lumbreras, Ramon Baldrich, Maria Vanrell, Joan Serrat, & Juan J. Villanueva. (1999). Multiresolution colour texture representations for tile classification.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Visual behaviours for binocular navigation with autonomous systems..
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A. Pujol, Jordi Vitria, Petia Radeva, Xavier Binefa, Robert Benavente, Ernest Valveny, et al. (1999). Real time pharmaceutical product recognition using color and shape indexing. In Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing..
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1997). A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Machine Vision and Applications, 9, 262–271.
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Gaze control in a binocular robot systems.
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Felipe Lumbreras, Ramon Baldrich, Maria Vanrell, Joan Serrat, & Juan J. Villanueva. (1999). Multiresolution texture classification of ceramic tiles. In Recent Research developments in optical engineering, Research Signpost, 2: 213–228.
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Robert Benavente, M.C. Olive, Maria Vanrell, & Ramon Baldrich. (1999). Colour Perception: A Simple Method for Colour Naming..
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Robert Benavente. (1999). Dealing with colour variability: application to a colour naming task.
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Jordi Vitria, Petia Radeva, X. Binefa, A. Pujol, Ernest Valveny, Robert Benavente, et al. (1999). Real time recognition of pharmaceutical products by subspace methods.
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