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| Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
 
 
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| Title | Improving Color Constancy by Photometric Edge Weighting | Type | Journal Article | |||
| Year | 2012 | Publication  |
IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI | |
| Volume | 34 | Issue | 5 | Pages | 918-929 | |
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| Abstract | : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. | |||||
| Address | Los Alamitos; CA; USA; | |||||
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| Series Volume | Series Issue | Edition | ||||
| ISSN | 0162-8828 | ISBN | Medium | |||
| Area | Expedition | Conference | ||||
| Notes | CIC;ISE | Approved | no | |||
| Call Number | Admin @ si @ GGW2012 | Serial | 1850 | |||
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| Author | Jaime Moreno; Xavier Otazu |
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| Title | Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder | Type | Conference Article | |||
| Year | 2011 | Publication |
IEEE International Conference on Multimedia and Expo | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-6 | |||
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| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. | |||||
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| ISSN | 1945-7871 | ISBN | 978-1-61284-348-3 | Medium | ||
| Area | Expedition | Conference | ICME | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MoO2011a | Serial | 2176 | |||
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| Author | Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang |
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| 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 | ||
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| 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 | |||||
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| Area | Expedition | Conference | ICASSP | |||
| Notes | CIC; MACO | Approved | no | |||
| Call Number | Admin @ si @ XHV2023 | Serial | 3909 | |||
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| Author | Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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| Title | Illuminant Invariant Model-Based Road Segmentation | Type | Conference Article | |||
| Year | 2008 | Publication |
IEEE Intelligent Vehicles Symposium, | Abbreviated Journal | ||
| Volume | Issue | Pages | 1155–1180 | |||
| Keywords | road detection | |||||
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| Address | Eindhoven (The Netherlands) | |||||
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| Notes | ADAS;CIC | Approved | no | |||
| Call Number | ADAS @ adas @ ALB2008 | Serial | 1045 | |||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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| Title | Saliency Estimation Using a Non-Parametric Low-Level Vision Model | Type | Conference Article | |||
| Year | 2011 | Publication |
IEEE conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 433-440 | |||
| Keywords | Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms | |||||
| Abstract | Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. | |||||
| Address | Colorado Springs | |||||
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| ISSN | 1063-6919 | ISBN | 978-1-4577-0394-2 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MVO2011 | Serial | 1757 | |||
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| Author | Rahat Khan; Joost Van de Weijer; Fahad Shahbaz Khan; Damien Muselet; christophe Ducottet; Cecile Barat |
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| Title | Discriminative Color Descriptors | Type | Conference Article | |||
| Year | 2013 | Publication |
IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 2866 - 2873 | |||
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| Abstract | Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200. | |||||
| Address | Portland; Oregon; June 2013 | |||||
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| Series Volume | Series Issue | Edition | ||||
| ISSN | 1063-6919 | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC; 600.048 | Approved | no | |||
| Call Number | Admin @ si @ KWK2013a | Serial | 2262 | |||
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| Author | Ivet Rafegas; Maria Vanrell |
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| Title | Color representation in CNNs: parallelisms with biological vision | Type | Conference Article | |||
| Year | 2017 | Publication |
ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision | Abbreviated Journal | ||
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| Abstract | Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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| Address | Venice; Italy; October 2017 | |||||
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| Area | Expedition | Conference | ICCV-MBCC | |||
| Notes | CIC; 600.087; 600.051 | Approved | no | |||
| Call Number | Admin @ si @ RaV2017 | Serial | 2984 | |||
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| Author | Sandra Jimenez; Xavier Otazu; Valero Laparra; Jesus Malo |
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| 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 | |||
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| 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 | |||||
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| Area | Expedition | Conference | HVEI | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ JOL2013 | Serial | 2240 | |||
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| Author | Partha Pratim Roy; Eduard Vazquez; Josep Llados; Ramon Baldrich; Umapada Pal |
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| Title | A System to Segment Text and Symbols from Color Maps | Type | Book Chapter | |||
| Year | 2008 | Publication |
Graphics Recognition. Recent Advances and New Opportunities | Abbreviated Journal | ||
| Volume | 5046 | Issue | Pages | 245-256 | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | |||
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| Notes | DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ RVL2008 | Serial | 1005 | |||
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| Author | Susana Alvarez; Xavier Otazu; Maria Vanrell |
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| Title | Image Segmentation Based on Inter-Feature Distance Maps | Type | Book Chapter | |||
| Year | 2005 | Publication |
Frontiers in Artificial Intelligence and Applications, IOS Press, 131: 75–82 | Abbreviated Journal | ||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ AOV2005 | Serial | 569 | |||
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| Author | Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez |
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| Title | Adaptation of a computer programming course to the EXHE requirements: evaluation five years later | Type | Miscellaneous | |||
| Year | 2012 | Publication |
European Journal of Engineering Education | Abbreviated Journal | ||
| Volume | 37 | Issue | 3 | Pages | 243-254 | |
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| Notes | DAG; CIC; OR; invisible;MV | Approved | no | |||
| Call Number | Admin @ si @ VBL2012 | Serial | 2070 | |||
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| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
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| Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | |||
| Year | 2015 | Publication |
European Conference on Visual Perception ECVP2015 | Abbreviated Journal | ||
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| Address | Liverpool; uk; August 2015 | |||||
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| Area | Expedition | Conference | ECVP | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ POW2015 | Serial | 2633 | |||
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| Author | Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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| Title | Categorical Focal Colours are Structurally Invariant Under Illuminant Changes | Type | Conference Article | |||
| Year | 2011 | Publication |
European Conference on Visual Perception | Abbreviated Journal | ||
| Volume | Issue | Pages | 196 | |||
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| Abstract | The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes. | |||||
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| Series Editor | Series Title | Perception 40 | Abbreviated Series Title | |||
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| Area | Expedition | Conference | ECVP | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RPV2011 | Serial | 1867 | |||
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| Author | Ivet Rafegas; Maria Vanrell |
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| Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | |||
| Year | 2016 | Publication |
European Conference on Visual Perception | Abbreviated Journal | ||
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| Address | Barcelona; Spain; August 2016 | |||||
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| Area | Expedition | Conference | ECVP | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RaV2016b | Serial | 2895 | |||
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| Author | C. Alejandro Parraga |
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| 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. | |||||
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| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Editor | Dieter Jaeger; Ranu Jung | ||
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| ISSN | ISBN | 978-1-4614-7320-6 | Medium | |||
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| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Par2014 | Serial | 2512 | |||
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| Author | C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger |
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| Title | Limitations of visual gamma corrections in LCD displays | Type | Journal Article | |||
| Year | 2014 | Publication |
Displays | Abbreviated Journal | Dis | |
| Volume | 35 | Issue | 5 | Pages | 227–239 | |
| Keywords | Display calibration; Psychophysics; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration | |||||
| Abstract | A method for estimating the non-linear gamma transfer function of liquid–crystal displays (LCDs) without the need of a photometric measurement device was described by Xiao et al. (2011) [1]. It relies on observer’s judgments of visual luminance by presenting eight half-tone patterns with luminances from 1/9 to 8/9 of the maximum value of each colour channel. These half-tone patterns were distributed over the screen both over the vertical and horizontal viewing axes. We conducted a series of photometric and psychophysical measurements (consisting in the simultaneous presentation of half-tone patterns in each trial) to evaluate whether the angular dependency of the light generated by three different LCD technologies would bias the results of these gamma transfer function estimations. Our results show that there are significant differences between the gamma transfer functions measured and produced by observers at different viewing angles. We suggest appropriate modifications to the Xiao et al. paradigm to counterbalance these artefacts which also have the advantage of shortening the amount of time spent in collecting the psychophysical measurements. | |||||
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| Notes | CIC; DAG; 600.052; 600.077; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ PRK2014 | Serial | 2511 | |||
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| Author | Jaime Moreno; Xavier Otazu |
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| Title | Image coder based on Hilbert scanning of embedded quadTrees | Type | Conference Article | |||
| Year | 2011 | Publication |
Data Compression Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | 470-470 | |||
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| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. | |||||
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| Area | Expedition | Conference | DCC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MoO2011b | Serial | 2177 | |||
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| Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta |
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| 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 | ||
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| 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 | |||||
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| Area | Expedition | Conference | CVPRW | |||
| Notes | CIC; LAMP; | Approved | no | |||
| Call Number | Admin @ si @ GAB2022 | Serial | 3700 | |||
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