| Home | [1–10] << 11 12 13 >> |
|
| Records | Links | |||||
|---|---|---|---|---|---|---|
| Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
 
 |
||||
| Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | |||
| Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 1090 - 1097 | |||
| Keywords | ||||||
| Abstract | Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second. |
|||||
| Address | Nottingham; UK; September 2014 | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor  |
||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ DKF2014 | Serial | 2509 | |||
| Permanent link to this record | ||||||
| Author | Fahad Shahbaz Khan; Shida Beigpour; Joost Van de Weijer; Michael Felsberg |
|
||||
| Title | Painting-91: A Large Scale Database for Computational Painting Categorization | Type | Journal Article | |||
| Year | 2014 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP | |
| Volume | 25 | Issue | 6 | Pages | 1385-1397 | |
| Keywords | ||||||
| Abstract | Computer analysis of visual art, especially paintings, is an interesting cross-disciplinary research domain. Most of the research in the analysis of paintings involve medium to small range datasets with own specific settings. Interestingly, significant progress has been made in the field of object and scene recognition lately. A key factor in this success is the introduction and availability of benchmark datasets for evaluation. Surprisingly, such a benchmark setup is still missing in the area of computational painting categorization. In this work, we propose a novel large scale dataset of digital paintings. The dataset consists of paintings from 91 different painters. We further show three applications of our dataset namely: artist categorization, style classification and saliency detection. We investigate how local and global features popular in image classification perform for the tasks of artist and style categorization. For both categorization tasks, our experimental results suggest that combining multiple features significantly improves the final performance. We show that state-of-the-art computer vision methods can correctly classify 50 % of unseen paintings to its painter in a large dataset and correctly attribute its artistic style in over 60 % of the cases. Additionally, we explore the task of saliency detection on paintings and show experimental findings using state-of-the-art saliency estimation algorithms. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor |
|||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | 0932-8092 | ISBN | Medium | |||
| Area | Expedition | Conference | ||||
| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ KBW2014 | Serial | 2510 | |||
| Permanent link to this record | ||||||
| Author | C. Alejandro Parraga; Jordi Roca; Dimosthenis Karatzas; Sophie Wuerger |
|
||||
| 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. | |||||
| 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; DAG; 600.052; 600.077; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ PRK2014 | Serial | 2511 | |||
| 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 | ||||||
| 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 | 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 | 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 | 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 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 | 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 | 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 | 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 | 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 | 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 | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
|
||||
| Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | |||
| Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | |||||
| Address | Virtual; September 2020 | |||||
| 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 | LIM | |||
| Notes | CIC; 600.118; 600.140; | Approved | no | |||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | |||
| Permanent link to this record | ||||||
| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
|
||||
| Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | |||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
|||||
| Address | Virtual; September 2020 | |||||
| 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; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | |||
| Permanent link to this record | ||||||
| Author | Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr |
|
||||
| Title | Colour-emotion associations in individuals with red-green colour blindness | Type | Journal Article | |||
| Year | 2021 | Publication | PeerJ | Abbreviated Journal | ||
| Volume | 9 | Issue | Pages | e11180 | ||
| Keywords | Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. | |||||
| Abstract | Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. | |||||
| 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; LAMP; 600.120; 600.128 | Approved | no | |||
| Call Number | Admin @ si @ JCP2021 | Serial | 3564 | |||
| Permanent link to this record | ||||||
| Author | Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
|
||||
| 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 |
||||
| 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 | |||
| Permanent link to this record | ||||||