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Author Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío edit   pdf
url  doi
openurl 
Title Matching visual induction effects on screens of different size Type Journal Article
Year 2021 Publication Journal of Vision Abbreviated Journal JOV  
Volume 21 Issue 6(10) Pages 1-22  
Keywords  
Abstract In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 Admin @ si @ CVM2021 Serial 3595  
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Author Graham D. Finlayson; Javier Vazquez; Fufu Fang edit   pdf
doi  openurl
Title The Discrete Cosine Maximum Ignorance Assumption Type Conference Article
Year 2021 Publication 29th Color and Imaging Conference Abbreviated Journal  
Volume Issue Pages 13-18  
Keywords  
Abstract the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between
wavelengths (many of the assumed reflectances are, in reality, not possible).
In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where
all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely.
Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets.
Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set.
 
Address Virtual; November 2021  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 FVF2021 Serial 3596  
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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio edit  url
doi  openurl
Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type Journal
Year 2021 Publication SMPTE Motion Imaging Journal Abbreviated Journal SMPTE  
Volume 130 Issue 4 Pages 53 - 65  
Keywords  
Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 SCV2021 Serial 3671  
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Author Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta edit   pdf
doi  openurl
Title Area Under the ROC Curve Maximization for Metric Learning Type Conference Article
Year 2022 Publication CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) Abbreviated Journal  
Volume Issue Pages  
Keywords Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition  
Abstract Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.  
Address New Orleans, USA; 20 June 2022  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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; LAMP; Approved no  
Call Number Admin @ si @ GAB2022 Serial 3700  
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Author Bojana Gajic; Ramon Baldrich edit  doi
openurl 
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.
 
Address Salt Lake City, USA; 22 June 2018  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 Bojana Gajic; Eduard Vazquez; Ramon Baldrich edit  url
openurl 
Title Evaluation of Deep Image Descriptors for Texture Retrieval Type Conference Article
Year 2017 Publication Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) Abbreviated Journal  
Volume Issue Pages 251-257  
Keywords Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation  
Abstract 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.
 
Address Porto, Portugal; 27 February – 1 March 2017  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference VISIGRAPP  
Notes CIC; 600.087 Approved no  
Call Number Admin @ si @ Serial 3710  
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Author Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte edit   pdf
url  openurl
Title NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement Type Conference Article
Year 2024 Publication 38th AAAI Conference on Artificial Intelligence Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract 3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference AAAI  
Notes CIC; MACO Approved no  
Call Number Admin @ si @ CVB2024 Serial 3872  
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Author Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown edit  url
openurl 
Title Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring Type Journal Article
Year 2023 Publication Computer Graphics Forum Abbreviated Journal CGF  
Volume Issue Pages  
Keywords  
Abstract Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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; MACO Approved no  
Call Number Admin @ si @ XVH2023 Serial 3883  
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Author Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray edit  url
openurl 
Title A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions Type Journal Article
Year 2023 Publication Journal of Vision Abbreviated Journal JV  
Volume 23 Issue 9 Pages 4817-4817  
Keywords  
Abstract A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes MACO; CIC Approved no  
Call Number Admin @ si @ PFV2023 Serial 3890  
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Author Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte edit   pdf
url  openurl
Title Perceptual image enhancement for smartphone real-time applications Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Abbreviated Journal  
Volume Issue Pages 1848-1858  
Keywords  
Abstract Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.  
Address Waikoloa; Hawai; USA; January 2023  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference WACV  
Notes MACO; CIC Approved no  
Call Number Admin @ si @ CVV2023 Serial 3900  
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Author Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang edit  url
doi  openurl
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 (up)  
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  
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Author Yawei Li; Yulun Zhang; Radu Timofte; Luc Van Gool; Zhijun Tu; Kunpeng Du; Hailing Wang; Hanting Chen; Wei Li; Xiaofei Wang; Jie Hu; Yunhe Wang; Xiangyu Kong; Jinlong Wu; Dafeng Zhang; Jianxing Zhang; Shuai Liu; Furui Bai; Chaoyu Feng; Hao Wang; Yuqian Zhang; Guangqi Shao; Xiaotao Wang; Lei Lei; Rongjian Xu; Zhilu Zhang; Yunjin Chen; Dongwei Ren; Wangmeng Zuo; Qi Wu; Mingyan Han; Shen Cheng; Haipeng Li; Ting Jiang; Chengzhi Jiang; Xinpeng Li; Jinting Luo; Wenjie Lin; Lei Yu; Haoqiang Fan; Shuaicheng Liu; Aditya Arora; Syed Waqas Zamir; Javier Vazquez; Konstantinos G. Derpanis; Michael S. Brown; Hao Li; Zhihao Zhao; Jinshan Pan; Jiangxin Dong; Jinhui Tang; Bo Yang; Jingxiang Chen; Chenghua Li; Xi Zhang; Zhao Zhang; Jiahuan Ren; Zhicheng Ji; Kang Miao; Suiyi Zhao; Huan Zheng; YanYan Wei; Kangliang Liu; Xiangcheng Du; Sijie Liu; Yingbin Zheng; Xingjiao Wu; Cheng Jin; Rajeev Irny; Sriharsha Koundinya; Vighnesh Kamath; Gaurav Khandelwal; Sunder Ali Khowaja; Jiseok Yoon; Ik Hyun Lee; Shijie Chen; Chengqiang Zhao; Huabin Yang; Zhongjian Zhang; Junjia Huang; Yanru Zhang edit  url
doi  openurl
Title NTIRE 2023 challenge on image denoising: Methods and results Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
Volume Issue Pages 1904-1920  
Keywords  
Abstract This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising.  
Address Vancouver; Canada; June 2023  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 MACO; CIC Approved no  
Call Number Admin @ si @ LZT2023 Serial 3910  
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Author Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde edit   pdf
url  openurl
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 (up)  
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  
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Author Trevor Canham; Javier Vazquez; D Long; Richard F. Murray; Michael S Brown edit   pdf
openurl 
Title Noise Prism: A Novel Multispectral Visualization Technique Type Journal Article
Year 2021 Publication 31st Color and Imaging Conference Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract A novel technique for visualizing multispectral images is proposed. Inspired by how prisms work, our method spreads spectral information over a chromatic noise pattern. This is accomplished by populating the pattern with pixels representing each measurement band at a count proportional to its measured intensity. The method is advantageous because it allows for lightweight encoding and visualization of spectral information
while maintaining the color appearance of the stimulus. A four alternative forced choice (4AFC) experiment was conducted to validate the method’s information-carrying capacity in displaying metameric stimuli of varying colors and spectral basis functions. The scores ranged from 100% to 20% (less than chance given the 4AFC task), with many conditions falling somewhere in between at statistically significant intervals. Using this data, color and texture difference metrics can be evaluated and optimized to predict the legibility of the visualization technique.
 
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up)  
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 MACO; CIC Approved no  
Call Number Admin @ si @ CVL2021 Serial 4000  
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Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu edit  doi
isbn  openurl
Title 3D Texton Spaces for color-texture retrieval Type Conference Article
Year 2010 Publication 7th International Conference on Image Analysis and Recognition Abbreviated Journal  
Volume 6111 Issue Pages 354–363  
Keywords  
Abstract Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.  
Address  
Corporate Author Thesis  
Publisher Springer Berlin Heidelberg Place of Publication Editor (up) A.C. Campilho and M.S. Kamel  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title LNCS  
Series Volume Series Issue Edition  
ISSN 0302-9743 ISBN 978-3-642-13771-6 Medium  
Area Expedition Conference ICIAR  
Notes CIC Approved no  
Call Number CAT @ cat @ ASV2010a Serial 1325  
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Author Joost Van de Weijer; Fahad Shahbaz Khan; Marc Masana edit   pdf
doi  isbn
openurl 
Title Interactive Visual and Semantic Image Retrieval Type Book Chapter
Year 2013 Publication Multimodal Interaction in Image and Video Applications Abbreviated Journal  
Volume 48 Issue Pages 31-35  
Keywords  
Abstract One direct consequence of recent advances in digital visual data generation and the direct availability of this information through the World-Wide Web, is a urgent demand for efficient image retrieval systems. The objective of image retrieval is to allow users to efficiently browse through this abundance of images. Due to the non-expert nature of the majority of the internet users, such systems should be user friendly, and therefore avoid complex user interfaces. In this chapter we investigate how high-level information provided by recently developed object recognition techniques can improve interactive image retrieval. Wel apply a bagof- word based image representation method to automatically classify images in a number of categories. These additional labels are then applied to improve the image retrieval system. Next to these high-level semantic labels, we also apply a low-level image description to describe the composition and color scheme of the scene. Both descriptions are incorporated in a user feedback image retrieval setting. The main objective is to show that automatic labeling of images with semantic labels can improve image retrieval results.  
Address  
Corporate Author Thesis  
Publisher Springer Berlin Heidelberg Place of Publication Editor (up) Angel Sappa; Jordi Vitria  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1868-4394 ISBN 978-3-642-35931-6 Medium  
Area Expedition Conference  
Notes CIC; 605.203; 600.048 Approved no  
Call Number Admin @ si @ WKC2013 Serial 2284  
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Author C. Alejandro Parraga edit  doi
isbn  openurl
Title Color Vision, Computational Methods for Type Book Chapter
Year 2014 Publication Encyclopedia of Computational Neuroscience Abbreviated Journal  
Volume Issue Pages 1-11  
Keywords Color computational vision; Computational neuroscience of color  
Abstract 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.  
Address  
Corporate Author Thesis  
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Editor (up) Dieter Jaeger; Ranu Jung  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN 978-1-4614-7320-6 Medium  
Area Expedition Conference  
Notes CIC; 600.074 Approved no  
Call Number Admin @ si @ Par2014 Serial 2512  
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Author C. Alejandro Parraga edit  isbn
openurl 
Title Perceptual Psychophysics Type Book Chapter
Year 2015 Publication Biologically-Inspired Computer Vision: Fundamentals and Applications Abbreviated Journal  
Volume Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor (up) G.Cristobal; M.Keil; L.Perrinet  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN 978-3-527-41264-8 Medium  
Area Expedition Conference  
Notes CIC; 600.074 Approved no  
Call Number Admin @ si @ Par2015 Serial 2600  
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