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Susana Alvarez, Anna Salvatella, Maria Vanrell, & Xavier Otazu. (2012). Low-dimensional and Comprehensive Color Texture Description. CVIU - Computer Vision and Image Understanding, 116(I), 54–67.
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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Danna Xue, Javier Vazquez, Luis Herranz, Yang Zhang, & Michael S Brown. (2023). Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring. CGF - Computer Graphics Forum, .
Abstract: Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.
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Robert Benavente, Ramon Baldrich, M.C. Olive, & Maria Vanrell. (2000). Colour Naming Considering the Colour Variability Problem..
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2004). Estimation of Fuzzy Sets for Computational Colour Categorization. Color Research and Application, 29(5):342–353 (IF: 0.739).
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2006). A data set for fuzzy colour naming. Color Research & Application, 31(1):48–56.
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Joost Van de Weijer, Robert Benavente, Maria Vanrell, Cordelia Schmid, Ramon Baldrich, Jacob Verbeek, et al. (2012). Color Naming. In Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & Jan-Mark Geusebroek (Eds.), Color in Computer Vision: Fundamentals and Applications (pp. 287–317). John Wiley & Sons, Ltd.
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Francesc Tous, Maria Vanrell, & Ramon Baldrich. (2004). Exploring Colour Constancy Solutions..
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Xavier Otazu, & Maria Vanrell. (2004). Building Perceived Colour Images..
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Robert Benavente, & Maria Vanrell. (2004). Fuzzy Colour Naming Based on Sigmoid Membership Functions..
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C. Alejandro Parraga. (2015). Perceptual Psychophysics. In G.Cristobal, M.Keil, & L.Perrinet (Eds.), Biologically-Inspired Computer Vision: Fundamentals and Applications.
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A. Richichi, O. Fors, M.T. Merino, Xavier Otazu, J. Nuñez, A. Prades, et al. (2006). The Calar Alto lunar occultation program: update and new results. Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088.
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O. Fors, A. Richichi, Xavier Otazu, & J. Nuñez. (2008). A new wavelet-based approach for the automated treatment of large sets of lunar occultation data. Astronomy and Astrohysics, 297–304.
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Justine Giroux, Mohammad Reza Karimi Dastjerdi, Yannick Hold-Geoffroy, Javier Vazquez, & Jean François Lalonde. (2024). Towards a Perceptual Evaluation Framework for Lighting Estimation. In Arxiv.
Abstract: rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms.
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Eduard Vazquez, Francesc Tous, Ramon Baldrich, & Maria Vanrell. (2006). n-Dimensional Distribution Reduction Preserving its Structure. In Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175.
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Javier Vazquez, Maria Vanrell, Anna Salvatella, & Eduard Vazquez. (2007). A colour space based on the image content. In Artificial Intelligence Research and Development, C. Angulo and L. Godo, pp 205–212 IOS Press.
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Aleksandr Setkov, Fabio Martinez Carillo, Michele Gouiffes, Christian Jacquemin, Maria Vanrell, & Ramon Baldrich. (2015). DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition. In Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II (Vol. 9475, pp. 463–473). LNCS. Springer International Publishing.
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.
Keywords: Projector-camera systems; Feature descriptors; Object recognition
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Antonio Lopez, J. Hilgenstock, A. Busse, Ramon Baldrich, Felipe Lumbreras, & Joan Serrat. (2008). Nightime Vehicle Detecion for Intelligent Headlight Control. In Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, (Vol. 5259, 113–124). LNCS.
Keywords: Intelligent Headlights; vehicle detection
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Robert Benavente, C. Alejandro Parraga, & Maria Vanrell. (2010). La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas. In 9th Congreso Nacional del Color (92–95).
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.
Keywords: Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos
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