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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Shadow Resistant Road Segmentation from a Mobile Monocular System |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 |
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road detection |
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Gerona (Spain) |
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ADAS @ adas @ ALB2007 |
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943 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Illuminant Invariant Model-Based Road Segmentation |
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Conference Article |
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2008 |
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IEEE Intelligent Vehicles Symposium, |
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1155–1180 |
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road detection |
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Eindhoven (The Netherlands) |
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ADAS @ adas @ ALB2008 |
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1045 |
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Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
![goto web page url](img/www.gif)
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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ADAS @ adas @ GBW2010 |
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1296 |
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Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Multiresolution colour texture representations for tile classification |
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Miscellaneous |
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Year |
1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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Bilbao |
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ADAS @ adas @ LBV1999a |
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3 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Multiresolution texture classification of ceramic tiles. |
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Book Chapter |
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1999 |
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Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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India |
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ADAS @ adas @ LBV1999b |
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45 |
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Author |
Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Nightime Vehicle Detecion for Intelligent Headlight Control |
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Conference Article |
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2008 |
Publication |
Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, |
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5259 |
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113–124 |
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Intelligent Headlights; vehicle detection |
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Juan-les-Pins, France |
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ACIVS |
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ADAS;CIC |
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ADAS @ adas @ LHB2008a |
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1098 |
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Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Temporal Coherence Analysis for Intelligent Headlight Control |
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Miscellaneous |
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2008 |
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2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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59–64 |
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Intelligent Headlights |
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IROS |
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ADAS;CIC |
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no |
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ADAS @ adas @ LHB2008b |
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1112 |
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Felipe Lumbreras; Xavier Roca; Daniel Ponsa; Robert Benavente; J. Martinez; Silvia Sanchez; Coen Antens; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Visual Inspection of Safety Belts |
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Conference Article |
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2001 |
Publication |
International Conference on Quality Control by Artificial Vision |
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2 |
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526–531 |
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France |
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QCAV |
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ADAS;ISE;CIC |
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ADAS @ adas @ LRP2001 |
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122 |
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Author |
Felipe Lumbreras; Joan Serrat; Ramon Baldrich; Maria Vanrell; Juan J. Villanueva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
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 @ adas @ LSB2001 |
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124 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; J. Martinez; Xavier Roca |
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Title |
Quality control of safety belts by machine vision inspection for real-time production |
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2003 |
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Optical Engineering, 42:1114–1120 (IF: 0.877) |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title |
Colour Normalisation Based on Background Information. |
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Miscellaneous |
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Year |
2001 |
Publication |
Proceeding ICIP 2001, IEEE International Conference on Image Processing |
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ICIP 2001 |
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1 |
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874–877 |
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Grecia. |
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ADAS;DAG;CIC |
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ADAS @ adas @ VLP2001 |
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167 |
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Author |
Bojana Gajic; Ramon Baldrich |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Cross-domain fashion image retrieval |
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Conference Article |
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2018 |
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CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) |
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19500-19502 |
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Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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Salt Lake City, USA; 22 June 2018 |
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CVPRW |
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CIC; 600.087 |
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Admin @ si @ |
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3709 |
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Author |
Bojana Gajic; Eduard Vazquez; Ramon Baldrich |
![goto web page url](img/www.gif)
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Title |
Evaluation of Deep Image Descriptors for Texture Retrieval |
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Conference Article |
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2017 |
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Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) |
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251-257 |
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Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation |
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The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures. |
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Porto, Portugal; 27 February – 1 March 2017 |
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VISIGRAPP |
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CIC; 600.087 |
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Admin @ si @ |
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3710 |
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Susana Alvarez; Maria Vanrell |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Texton theory revisited: a bag-of-words approach to combine textons |
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Journal Article |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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12 |
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4312-4325 |
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The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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0031-3203 |
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CIC |
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Admin @ si @ AlV2012a |
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2130 |
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Author |
Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Low-dimensional and Comprehensive Color Texture Description |
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Journal Article |
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2012 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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116 |
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I |
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54-67 |
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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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1077-3142 |
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Admin @ si @ ASV2012 |
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1827 |
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Author |
Ricard Balague |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Exploring the combination of color cues for intrinsic image decomposition |
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2014 |
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CVC Technical Report |
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178 |
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Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. |
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UAB; September 2014 |
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Master's thesis |
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CIC; 600.074 |
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Admin @ si @ Bal2014 |
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2579 |
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Author |
Shida Beigpour |
![find record details (via OpenURL) openurl](img/xref.gif)
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Physics-based Reflectance Estimation Applied to Recoloring |
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2009 |
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CVC Technical Report |
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137 |
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Computer Vision Center |
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Bellaterra, Barcelona |
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Admin @ si @ Bei2009 |
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2396 |
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Shida Beigpour |
![find record details (via OpenURL) openurl](img/xref.gif)
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Illumination and object reflectance modeling |
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2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information. |
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Barcelona |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Joost Van de Weijer;Ernest Valveny |
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Admin @ si @ Bei2013 |
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2267 |
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