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Author |
J.M. Sanchez; X. Binefa |
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Color normalization for digital video processing |
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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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Author |
J.M. Sanchez; X. Binefa |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color Normalization for Appearance Based Recognition of Video Key-frames. |
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Conference Article |
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2000 |
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15 th International Conference on Pattern Recognition |
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1 |
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815-818 |
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Barcelona. |
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ICPR |
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no |
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Admin @ si @ SaB2000 |
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220 |
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Author |
Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.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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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, descending order (down)](img/sort_desc.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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CIC |
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no |
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Admin @ si @ WBV2012 |
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2063 |
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Author |
Javier Vazquez; Maria Vanrell; Robert Benavente |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color names as a constraint for Computer Vision problems |
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Conference Article |
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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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CIC |
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no |
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CAT @ cat @ VVB2010 |
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1328 |
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Author |
Xim Cerda-Company; Xavier Otazu |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
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 |
Ivet Rafegas |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color in Visual Recognition: from flat to deep representations and some biological parallelisms |
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Book Whole |
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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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CIC |
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no |
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Admin @ si @ Raf2017 |
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3100 |
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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 |
David Guillamet |
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Color histogram classification using NMF |
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2001 |
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CVC Technical Report #57 |
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CVC (UAB) |
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Admin @ si @ Gui2001 |
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98 |
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Author |
Muhammad Anwer Rao |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color for Object Detection and Action Recognition |
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2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition.
In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection.
In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task.
Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images. |
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Barcelona |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Antonio Lopez;Joost Van de Weijer |
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ADAS |
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no |
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Admin @ si @ Rao2013 |
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2281 |
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Author |
Ramon Baldrich; Maria Vanrell; Robert Benavente; Anna Salvatella |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color Enhancement based on perceptual sharpening |
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Miscellaneous |
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2003 |
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Proceedings of the IEEE International Conference on Image Processing |
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Barcelona |
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CIC |
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CAT @ cat @ BVB2003 |
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370 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color encoding in biologically-inspired convolutional neural networks |
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Journal Article |
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2018 |
Publication |
Vision Research |
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VR |
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151 |
Issue |
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7-17 |
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Color coding; Computer vision; Deep learning; Convolutional neural networks |
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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. |
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CIC; 600.051; 600.087 |
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no |
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Admin @ si @RaV2018 |
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3114 |
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Author |
David Augusto Rojas; Joost Van de Weijer; Theo Gevers |
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Title ![sorted by Title field, descending order (down)](img/sort_desc.gif) |
Color Edge Saliency Boosting using Natural Image Statistics |
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Conference Article |
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2010 |
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5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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228–234 |
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State of the art methods for image matching, content-based retrieval and recognition use local features. Most of these still exploit only the luminance information for detection. The color saliency boosting algorithm has provided an efficient method to exploit the saliency of color edges based on information theory. However, during the design of this algorithm, some issues were not addressed in depth: (1) The method has ignored the underlying distribution of derivatives in natural images. (2) The dependence of information content in color-boosted edges on its spatial derivatives has not been quantitatively established. (3) To evaluate luminance and color contributions to saliency of edges, a parameter gradually balancing both contributions is required.
We introduce a novel algorithm, based on the principles of independent component analysis, which models the first order derivatives of color natural images by a generalized Gaussian distribution. Furthermore, using this probability model we show that for images with a Laplacian distribution, which is a particular case of generalized Gaussian distribution, the magnitudes of color-boosted edges reflect their corresponding information content. In order to evaluate the impact of color edge saliency in real world applications, we introduce an extension of the Laplacian-of-Gaussian detector to color, and the performance for image matching is evaluated. Our experiments show that our approach provides more discriminative regions in comparison with the original detector. |
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Joensuu, Finland |
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9781617388897 |
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CAT @ cat @ RWG2010 |
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1306 |
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Author |
Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Color descriptor for content-based drawing retrieval |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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267 - 271 |
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Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAG; 600.056; 600.077 |
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Admin @ si @ RKB2014 |
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2479 |
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Miguel Oliveira; Angel Sappa; V. Santos |
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Color Correction using 3D Gaussian Mixture Models |
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2012 |
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9th International Conference on Image Analysis and Recognition |
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7324 |
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97-106 |
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The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
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Springer Berlin Heidelberg |
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0302-9743 |
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10.1007/978-3-642-31295-3_12 |
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ICIAR |
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Admin @ si @ OSS2012a |
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2015 |
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