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Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell | ||||
Title | Names and Shades of Color for Intrinsic Image Estimation | Type | Conference Article | ||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 278-285 | ||
Keywords | |||||
Abstract | In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. | ||||
Address ![]() |
Providence, Rhode Island | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE Xplore | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
Area | Expedition | Conference | CVPR | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ SPB2012 | Serial | 2026 | ||
Permanent link to this record | |||||
Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez | ||||
Title | Color Attributes for Object Detection | Type | Conference Article | ||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3306-3313 | ||
Keywords | pedestrian detection | ||||
Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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Address ![]() |
Providence; Rhode Island; USA; | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE Xplore | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
Area | Expedition | Conference | CVPR | ||
Notes | ADAS; CIC; | Approved | no | ||
Call Number | Admin @ si @ KRW2012 | Serial | 1935 | ||
Permanent link to this record | |||||
Author | Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang | ||||
Title | Burst Perception-Distortion Tradeoff: Analysis and Evaluation | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Conference on Acoustics, Speech and Signal Processing | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment. | ||||
Address ![]() |
Rodhes Islands; Greece; June 2023 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICASSP | ||
Notes | CIC; MACO | Approved | no | ||
Call Number | Admin @ si @ XHV2023 | Serial | 3909 | ||
Permanent link to this record | |||||
Author | Xavier Otazu; Maria Vanrell | ||||
Title | Several lightness induction effects with a computational multiresolution wavelet framework | Type | Journal | ||
Year | 2006 | Publication | 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | |||||
Address ![]() |
Saint-Petersburg (Russia) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ OtV2006 | Serial | 659 | ||
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Author | Bojana Gajic; Ramon Baldrich | ||||
Title | Cross-domain fashion image retrieval | Type | Conference Article | ||
Year | 2018 | Publication | CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) | Abbreviated Journal | |
Volume | Issue | Pages | 19500-19502 | ||
Keywords | |||||
Abstract | 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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Address ![]() |
Salt Lake City, USA; 22 June 2018 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | CIC; 600.087 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3709 | ||
Permanent link to this record | |||||
Author | Maria Vanrell; Jordi Vitria | ||||
Title | Mathematical Morphology, Granulometries and Texture Perception. | Type | Miscellaneous | ||
Year | 1993 | Publication | SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Address ![]() |
San Diego; CA; USA | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | OR;CIC;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ VaV1993 | Serial | 178 | ||
Permanent link to this record | |||||
Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Color spaces emerging from deep convolutional networks | Type | Conference Article | ||
Year | 2016 | Publication | 24th Color and Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 225-230 | ||
Keywords | |||||
Abstract | 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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Address ![]() |
San Diego; USA; November 2016 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ RaV2016a | Serial | 2894 | ||
Permanent link to this record | |||||
Author | Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez | ||||
Title | Harmony Potentials for Joint Classification and Segmentation | Type | Conference Article | ||
Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 3280–3287 | ||
Keywords | |||||
Abstract | 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. | ||||
Address ![]() |
San Francisco CA, USA | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
Area | Expedition | Conference | CVPR | ||
Notes | ADAS;CIC;ISE | Approved | no | ||
Call Number | ADAS @ adas @ GBW2010 | Serial | 1296 | ||
Permanent link to this record | |||||
Author | Sandra Jimenez; Xavier Otazu; Valero Laparra; Jesus Malo | ||||
Title | Chromatic induction and contrast masking: similar models, different goals? | Type | Conference Article | ||
Year | 2013 | Publication | Human Vision and Electronic Imaging XVIII | Abbreviated Journal | |
Volume | 8651 | Issue | Pages | ||
Keywords | |||||
Abstract | Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. | ||||
Address ![]() |
San Francisco CA; USA; February 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | HVEI | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ JOL2013 | Serial | 2240 | ||
Permanent link to this record | |||||
Author | Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde | ||||
Title | Towards a Perceptual Evaluation Framework for Lighting Estimation | Type | Conference Article | ||
Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. | ||||
Address ![]() |
Seattle; USA; June 2024 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPR | ||
Notes | MACO; CIC | Approved | no | ||
Call Number | Admin @ si @ GDH2024 | Serial | 3999 | ||
Permanent link to this record | |||||
Author | Marc Serra | ||||
Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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Address ![]() |
September 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Robert Benavente;Olivier Penacchio | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-943427-4-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC; 600.074 | Approved | no | ||
Call Number | Admin @ si @ Ser2015 | Serial | 2688 | ||
Permanent link to this record | |||||
Author | Hassan Ahmed Sial | ||||
Title | Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation | Type | Book Whole | ||
Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | In this thesis, we explore how to estimate the effects of the light interacting with the scene objects from a single image. To achieve this goal, we focus on recovering intrinsic components like reflectance, shading, or light properties such as color and position using deep architectures. The success of these approaches relies on training on large and diversified image datasets. Therefore, we present several contributions on this such as: (a) a data-augmentation technique; (b) a ground-truth for an existing multi-illuminant dataset; (c) a family of synthetic datasets, SID for Surreal Intrinsic Datasets, with diversified backgrounds and coherent light conditions; and (d) a practical pipeline to create hybrid ground-truths to overcome the complexity of acquiring realistic light conditions in a massive way. In parallel with the creation of datasets, we trained different flexible encoder-decoder deep architectures incorporating physical constraints from the image formation models.
In the last part of the thesis, we apply all the previous experience to two different problems. Firstly, we create a large hybrid Doc3DShade dataset with real shading and synthetic reflectance under complex illumination conditions, that is used to train a two-stage architecture that improves the character recognition task in complex lighting conditions of unwrapped documents. Secondly, we tackle the problem of single image scene relighting by extending both, the SID dataset to present stronger shading and shadows effects, and the deep architectures to use intrinsic components to estimate new relit images. |
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Address ![]() |
September 2021 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Maria Vanrell;Ramon Baldrich | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-122714-8-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC; | Approved | no | ||
Call Number | Admin @ si @ Sia2021 | Serial | 3607 | ||
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Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell | ||||
Title | High-Level Clothes Description Based on Color-Texture and Structural Features | Type | Book Chapter | ||
Year | 2003 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | |
Volume | 2652 | Issue | Pages | 108–116 | |
Keywords | |||||
Abstract | This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. | ||||
Address ![]() |
Springer-Verlag | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | DAG;CIC | Approved | no | ||
Call Number | CAT @ cat @ BTL2003a | Serial | 368 | ||
Permanent link to this record | |||||
Author | Anna Salvatella; Maria Vanrell; Ramon Baldrich | ||||
Title | Subtexture Components for Texture Description | Type | Miscellaneous | ||
Year | 2003 | Publication | Lecture Notes in Computer Science, vol 2652, pp 884–892 | Abbreviated Journal | |
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Abstract | |||||
Address ![]() |
Springer-Verlag | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ SVR2003 | Serial | 421 | ||
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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg | ||||
Title | Scale Coding Bag-of-Words for Action Recognition | Type | Conference Article | ||
Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1514-1519 | ||
Keywords | |||||
Abstract | Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods. |
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Address ![]() |
Stockholm; August 2014 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICPR | ||
Notes | CIC; LAMP; 601.240; 600.074; 600.079 | Approved | no | ||
Call Number | Admin @ si @ KWB2014 | Serial | 2450 | ||
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Author | Eduard Vazquez; Ramon Baldrich | ||||
Title | Colour Image Segmentation in Presence of Shadows | Type | Conference Article | ||
Year | 2008 | Publication | 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings | Abbreviated Journal | |
Volume | Issue | Pages | 383–387 | ||
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Abstract | |||||
Address ![]() |
Terrassa (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CGIV08 | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ VaB2008 | Serial | 966 | ||
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Author | Javier Vazquez; Maria Vanrell; Ramon Baldrich | ||||
Title | Towards a Psychophysical Evaluation of Colour Constancy Algorithms | Type | Conference Article | ||
Year | 2008 | Publication | 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings | Abbreviated Journal | |
Volume | Issue | Pages | 372–377 | ||
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Abstract | |||||
Address ![]() |
Terrassa (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CGIV08 | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ VVB2008a | Serial | 968 | ||
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Author | C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich | ||||
Title | Modelling Inter-Colour Regions of Colour Naming Space | Type | Conference Article | ||
Year | 2008 | Publication | 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings | Abbreviated Journal | |
Volume | Issue | Pages | 218–222 | ||
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Abstract | |||||
Address ![]() |
Terrassa (Spain) | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CGIV08 | ||
Notes | CAT;CIC | Approved | no | ||
Call Number | CAT @ cat @ PBV2008 | Serial | 969 | ||
Permanent link to this record |