| Home | << 1 2 3 4 5 6 7 8 9 10 >> [11–13] |
|
| Records | Links | |||||
|---|---|---|---|---|---|---|
| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
 
 |
||||
| Title | Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects | Type | Journal Article | |||
| Year | 2020 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A | |
| Volume | 37 | Issue | 1 | Pages | 1-15 | |
| Keywords | ||||||
| Abstract | Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. | |||||
| Address | ||||||
| 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; 600.140; 600.12; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ SBV2019 | Serial  |
3311 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
|
||||
| Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | |||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL | |
| Volume | 136 | Issue | Pages | 318-325 | ||
| Keywords | ||||||
| Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | |||||
| Address | ||||||
| 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; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ RVL2019 | Serial |
3310 | |||
| Permanent link to this record | ||||||
| Author | Bojana Gajic; Ariel Amato; Ramon Baldrich; Carlo Gatta |
|
||||
| Title | Bag of Negatives for Siamese Architectures | Type | Conference Article | |||
| Year | 2019 | Publication | 30th British Machine Vision Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy. | |||||
| Address | Cardiff; United Kingdom; September 2019 | |||||
| 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 | BMVC | |||
| Notes | CIC; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ GAB2019b | Serial |
3263 | |||
| Permanent link to this record | ||||||
| Author | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
|
||||
| Title | Color-based data augmentation for Reflectance Estimation | Type | Conference Article | |||
| Year | 2018 | Publication | 26th Color Imaging Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | 284-289 | |||
| Keywords | ||||||
| Abstract | Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. | |||||
| Address | Vancouver; November 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 | CIC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ SSB2018a | Serial |
3129 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas; Maria Vanrell |
|
||||
| Title | Color encoding in biologically-inspired convolutional neural networks | Type | Journal Article | |||
| Year | 2018 | Publication | Vision Research | Abbreviated Journal | VR | |
| Volume | 151 | Issue | Pages | 7-17 | ||
| Keywords | Color coding; Computer vision; Deep learning; Convolutional neural networks | |||||
| Abstract | Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. | |||||
| Address | ||||||
| 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; 600.051; 600.087 | Approved | no | |||
| Call Number | Admin @ si @RaV2018 | Serial |
3114 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas |
|
||||
| Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | |||
| Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
|||||
| Address | November 2017 | |||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | ||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | 978-84-945373-7-0 | Medium | |||
| Area | Expedition | Conference | ||||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Raf2017 | Serial |
3100 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas; Maria Vanrell |
|
||||
| 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 | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| 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). |
|||||
| Address | Venice; Italy; October 2017 | |||||
| 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 | ICCV-MBCC | |||
| Notes | CIC; 600.087; 600.051 | Approved | no | |||
| Call Number | Admin @ si @ RaV2017 | Serial |
2984 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas; Maria Vanrell |
|
||||
| Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | |||
| Year | 2016 | Publication | European Conference on Visual Perception | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | Barcelona; Spain; August 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 | ECVP | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RaV2016b | Serial |
2895 | |||
| 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. |
|||||
| 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 | Jordi Roca |
|
||||
| Title | Constancy and inconstancy in categorical colour perception | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception. | |||||
| Address | ||||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Place of Publication | Editor | Maria Vanrell;C. Alejandro Parraga | |||
| 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 @ Roc2012 | Serial |
2893 | |||
| Permanent link to this record | ||||||
| Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez |
|
||||
| Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | |||
| Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A | |
| Volume | 34 | Issue | 5 | Pages | 827-837 | |
| Keywords | ||||||
| Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | |||||
| Address | ||||||
| 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; 600.087 | Approved | no | |||
| Call Number | Admin @ si @ RVB2017 | Serial |
2892 | |||
| Permanent link to this record | ||||||
| Author | Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
|
||||
| Title | DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition | Type | Conference Article | |||
| Year | 2015 | Publication | Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II | Abbreviated Journal | ||
| Volume | 9475 | Issue | Pages | 463-473 | ||
| Keywords | Projector-camera systems; Feature descriptors; Object recognition | |||||
| Abstract | Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Springer International Publishing | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | |||
| Series Volume | Series Issue | Edition | ||||
| ISSN | 0302-9743 | ISBN | 978-3-319-27862-9 | Medium | ||
| Area | Expedition | Conference | ISVC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ SMG2015 | Serial |
2736 | |||
| 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. |
|||||
| 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 | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
|
||||
| Title | An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort | Type | Conference Article | |||
| Year | 2015 | Publication | Barcelona Computational, Cognitive and Systems Neuroscience | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | Barcelona; June 2015 | |||||
| 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 | BARCCSYN | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ OPC2015b | Serial |
2634 | |||
| Permanent link to this record | ||||||
| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
|
||||
| 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 | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | Liverpool; uk; August 2015 | |||||
| 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 | ECVP | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ POW2015 | Serial |
2633 | |||
| Permanent link to this record | ||||||
| Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
|
||||
| Title | Brightness and colour induction through contextual influences in V1 | Type | Conference Article | |||
| Year | 2015 | Publication | Scottish Vision Group 2015 SGV2015 | Abbreviated Journal | ||
| Volume | 12 | Issue | 9 | Pages | 1208-2012 | |
| Keywords | ||||||
| Abstract | ||||||
| Address | Carnoustie; Scotland; March 2015 | |||||
| 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 | SGV | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ OPC2015a | Serial |
2632 | |||
| Permanent link to this record | ||||||
| Author | C. Alejandro Parraga |
|
||||
| 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 | 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 | |||
| Permanent link to this record | ||||||
| Author | Ricard Balague |
|
||||
| Title | Exploring the combination of color cues for intrinsic image decomposition | Type | Report | |||
| Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | ||
| Volume | 178 | Issue | Pages | |||
| Keywords | ||||||
| Abstract | 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. | |||||
| Address | UAB; September 2014 | |||||
| Corporate Author | Thesis | Master's 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; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Bal2014 | Serial |
2579 | |||
| Permanent link to this record | ||||||