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C. Alejandro Parraga. (2014). Color Vision, Computational Methods for. In Dieter Jaeger, & Ranu Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–11). Springer-Verlag Berlin Heidelberg.
Abstract: The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments.
Keywords: Color computational vision; Computational neuroscience of color
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Joost Van de Weijer, Fahad Shahbaz Khan, & Marc Masana. (2013). Interactive Visual and Semantic Image Retrieval. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 31–35). Springer Berlin Heidelberg.
Abstract: One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results.
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Susana Alvarez, Anna Salvatella, Maria Vanrell, & Xavier Otazu. (2010). 3D Texton Spaces for color-texture retrieval. In A.C. Campilho and M.S. Kamel (Ed.), 7th International Conference on Image Analysis and Recognition (Vol. 6111, 354–363). LNCS. Springer Berlin Heidelberg.
Abstract: Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.
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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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Ramon Baldrich. (2001). Perceptual approach to a computational colour-texture representation for surface inspection..
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Robert Benavente, & Maria Vanrell. (2001). A colour naming experiment.
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O. Fors, Xavier Otazu, & J. Nuñez. (2001). Fusion Mediante Wavelets de Imagenes Spot-pan y del Satelite Tailandes TMSAT..
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Felipe Lumbreras, Xavier Roca, Daniel Ponsa, Robert Benavente, Judit Martinez, Silvia Sanchez, et al. (2001). Visual Inspection of Safety Belts. In International Conference on Quality Control by Artificial Vision (Vol. 2, 526–531).
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Felipe Lumbreras, Joan Serrat, Ramon Baldrich, Maria Vanrell, & Juan J. Villanueva. (2001). Color Texture Recognition Through Multiresolution Features.
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Xavier Otazu, & J. Nuñez. (2001). Algoritmo de Clasificacion no Supervisada Basado en Wavelets..
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