Xavier Otazu, Maria Vanrell, & C. Alejandro Parraga. (2008). Multiresolution Wavelet Framework Models Brightness Induction Effects. VR - Vision Research, 733–751.
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Ivet Rafegas, & Maria Vanrell. (2018). Color encoding in biologically-inspired convolutional neural networks. VR - Vision Research, 151, 7–17.
Abstract: Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations.
Keywords: Color coding; Computer vision; Deep learning; Convolutional neural networks
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X. Binefa, Jordi Vitria, & Maria Vanrell. (1992). Reconstruccion tridimensional de imagenes Microscopicas..
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Eduard Vazquez, & Maria Vanrell. (2008). Eines per al desenvolupament de competencies de enginyeria en un assignatura de Intel·ligencia Artificial.
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1993). A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries..
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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception..
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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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Felipe Lumbreras, Joan Serrat, Ramon Baldrich, Maria Vanrell, & Juan J. Villanueva. (2001). Color Texture Recognition Through Multiresolution Features.
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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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Ramon Baldrich, Maria Vanrell, Robert Benavente, & Anna Salvatella. (2003). Color Enhancement based on perceptual sharpening.
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Jaime Moreno, Xavier Otazu, & Maria Vanrell. (2010). Contribution of CIWaM in JPEG2000 Quantization for Color Images. In Proceedings of The CREATE 2010 Conference (132–136).
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(ChromaticInductionWaveletModel).
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Javier Vazquez, Maria Vanrell, & Robert Benavente. (2010). Color names as a constraint for Computer Vision problems. In Proceedings of The CREATE 2010 Conference (324–328).
Abstract: Computer Vision Problems are usually ill-posed. Constraining de gamut of possible solutions is then a necessary step. Many constrains for different problems have been developed during years. In this paper, we present a different way of constraining some of these problems: the use of color names. In particular, we will focus on segmentation, representation ans constancy.
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Fahad Shahbaz Khan, Joost Van de Weijer, & Maria Vanrell. (2010). Who Painted this Painting? In Proceedings of The CREATE 2010 Conference (329–333).
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