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Theo Gevers; Arjan Gijsenij; Joost Van de Weijer; J.M. Geusebroek |
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Color in Computer Vision: Fundamentals and Applications |
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2012 |
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Color in Computer Vision: Fundamentals and Applications |
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The Wiley-IS&T Series in Imaging Science and Technology |
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978-0-470-89084-4 |
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ALTRES;ISE |
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no |
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Admin @ si @ GGG2012a |
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2068 |
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Author |
Ivet Rafegas |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color in Visual Recognition: from flat to deep representations and some biological parallelisms |
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2017 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.
Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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November 2017 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Maria Vanrell |
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978-84-945373-7-0 |
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Admin @ si @ Raf2017 |
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3100 |
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Xim Cerda-Company; Xavier Otazu |
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Color induction in equiluminant flashed stimuli |
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Journal Article |
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2019 |
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Journal of the Optical Society of America A |
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JOSA A |
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36 |
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1 |
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22-31 |
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Color induction is the influence of the surrounding color (inducer) on the perceived color of a central region. There are two different types of color induction: color contrast (the color of the central region shifts away from that of the inducer) and color assimilation (the color shifts towards the color of the inducer). Several studies on these effects have used uniform and striped surrounds, reporting color contrast and color assimilation, respectively. Other authors [J. Vis. 12(1), 22 (2012) [CrossRef] ] have studied color induction using flashed uniform surrounds, reporting that the contrast is higher for shorter flash duration. Extending their study, we present new psychophysical results using both flashed and static (i.e., non-flashed) equiluminant stimuli for both striped and uniform surrounds. Similarly to them, for uniform surround stimuli we observed color contrast, but we did not obtain the maximum contrast for the shortest (10 ms) flashed stimuli, but for 40 ms. We only observed this maximum contrast for red, green, and lime inducers, while for a purple inducer we obtained an asymptotic profile along the flash duration. For striped stimuli, we observed color assimilation only for the static (infinite flash duration) red–green surround inducers (red first inducer, green second inducer). For the other inducers’ configurations, we observed color contrast or no induction. Since other studies showed that non-equiluminant striped static stimuli induce color assimilation, our results also suggest that luminance differences could be a key factor to induce it. |
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NEUROBIT; 600.120; 600.128 |
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Admin @ si @ CeO2019 |
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3226 |
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Author |
Javier Vazquez; Maria Vanrell; Robert Benavente |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color names as a constraint for Computer Vision problems |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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324–328 |
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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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Gjovik (Norway) |
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CREATE |
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CAT @ cat @ VVB2010 |
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1328 |
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Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color Naming |
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2012 |
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Color in Computer Vision: Fundamentals and Applications |
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17 |
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287-317 |
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John Wiley & Sons, Ltd. |
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Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek |
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Admin @ si @ WBV2012 |
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2063 |
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Author |
Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color Naming for Multi-Color Fashion Items |
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Conference Article |
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2018 |
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6th World Conference on Information Systems and Technologies |
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747 |
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64-73 |
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Deep learning; Color; Multi-label |
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There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. |
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Naples; March 2018 |
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WORLDCIST |
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LAMP; 600.109; 601.309; 600.120 |
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Admin @ si @ YWR2018 |
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3161 |
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J.M. Sanchez; X. Binefa |
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Color Normalization for Appearance Based Recognition of Video Key-frames. |
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2000 |
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15 th International Conference on Pattern Recognition |
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815-818 |
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Barcelona. |
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ICPR |
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Admin @ si @ SaB2000 |
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220 |
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J.M. Sanchez; X. Binefa |
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Color normalization for digital video processing |
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Report |
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1999 |
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CVC Technical Report #37 |
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CVC (UAB) |
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Admin @ si @ SaB1999 |
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525 |
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Ivet Rafegas; Maria Vanrell |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color representation in CNNs: parallelisms with biological vision |
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2017 |
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ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision |
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Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN
[2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions
of them to quantify color tuning properties of artificial neurons to provide a classification of the network population.
We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first
layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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Venice; Italy; October 2017 |
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ICCV-MBCC |
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CIC; 600.087; 600.051 |
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Admin @ si @ RaV2017 |
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2984 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color spaces emerging from deep convolutional networks |
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2016 |
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24th Color and Imaging Conference |
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225-230 |
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Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
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San Diego; USA; November 2016 |
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Admin @ si @ RaV2016a |
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2894 |
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Author |
Felipe Lumbreras; Joan Serrat; Ramon Baldrich; Maria Vanrell; Juan J. Villanueva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color Texture Recognition Through Multiresolution Features |
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Miscellaneous |
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2001 |
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QCAV 2001 International Conference on Quality Control by Artificial Vision, France, 1:114–121. |
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ADAS;CIC |
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ADAS @ adas @ LSB2001 |
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124 |
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C. Alejandro Parraga |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color Vision, Computational Methods for |
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2014 |
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Encyclopedia of Computational Neuroscience |
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1-11 |
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Color computational vision; Computational neuroscience of color |
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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. |
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Springer-Verlag Berlin Heidelberg |
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Dieter Jaeger; Ranu Jung |
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978-1-4614-7320-6 |
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CIC; 600.074 |
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Admin @ si @ Par2014 |
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2512 |
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Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Color-based data augmentation for Reflectance Estimation |
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Conference Article |
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2018 |
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26th Color Imaging Conference |
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284-289 |
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Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. |
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Vancouver; November 2018 |
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Admin @ si @ SSB2018a |
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3129 |
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Abel Gonzalez-Garcia; Robert Benavente; Olivier Penacchio; Javier Vazquez; Maria Vanrell; C. Alejandro Parraga |
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Coloresia: An Interactive Colour Perception Device for the Visually Impaired |
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2013 |
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Multimodal Interaction in Image and Video Applications |
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48 |
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47-66 |
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A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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CIC; 600.052; 605.203 |
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Admin @ si @ GBP2013 |
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2266 |
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Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg |
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Coloring Action Recognition in Still Images |
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2013 |
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International Journal of Computer Vision |
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IJCV |
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105 |
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3 |
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205-221 |
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In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. |
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Springer US |
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0920-5691 |
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CIC; ADAS; 600.057; 600.048 |
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Admin @ si @ KRW2013 |
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2285 |
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