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| Author | A. Ruiz; Joost Van de Weijer; Xavier Binefa |
 
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| Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | |||
| Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | ||
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| Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | |||||
| Address | Nottingham; UK; September 2014 | |||||
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| Area | Expedition | Conference | BMVC | |||
| Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ RWB2014 | Serial | 2508 | |||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg |
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| Title | Scale Coding Bag-of-Words for Action Recognition | Type | Conference Article | |||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 1514-1519 | |||
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| Abstract | Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods. |
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| Address | Stockholm; August 2014 | |||||
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| Area | Expedition | Conference | ICPR | |||
| Notes | CIC; LAMP; 601.240; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ KWB2014 | Serial | 2450 | |||
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| Author | Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou |
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| Title | Multi-Illuminant Estimation with Conditional Random Fields | Type | Journal Article | |||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP | |
| Volume | 23 | Issue | 1 | Pages | 83-95 | |
| Keywords | color constancy; CRF; multi-illuminant | |||||
| Abstract | Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. | |||||
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| ISSN | 1057-7149 | ISBN | Medium | |||
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| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ BRW2014 | Serial | 2451 | |||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |
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| Title | Semantic Pyramids for Gender and Action Recognition | Type | Journal Article | |||
| Year | 2014 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP | |
| Volume | 23 | Issue | 8 | Pages | 3633-3645 | |
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| Abstract | Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. | |||||
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| ISSN | 1057-7149 | ISBN | Medium | |||
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| Notes | CIC; LAMP; 601.160; 600.074; 600.079;MILAB | Approved | no | |||
| Call Number | Admin @ si @ KWR2014 | Serial | 2507 | |||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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| Title | The Photometry of Intrinsic Images | Type | Conference Article | |||
| Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 1494-1501 | |||
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| Abstract | Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. | |||||
| Address | Columbus; Ohio; USA; June 2014 | |||||
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| Area | Expedition | Conference | CVPR | |||
| Notes | CIC; 600.052; 600.051; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ SPB2014 | Serial | 2506 | |||
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| Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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| 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 | |||
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| 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. |
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| Address | Nottingham; UK; September 2014 | |||||
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| Area | Expedition | Conference | CVPR | |||
| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ DKF2014 | Serial | 2509 | |||
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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 | Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu |
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| Title | Which tone-mapping is the best? A comparative study of tone-mapping perceived quality | Type | Abstract | |||
| Year | 2014 | Publication | Perception | Abbreviated Journal | ||
| Volume | 43 | Issue | Pages | 106 | ||
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| Abstract | Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings. |
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| Area | Expedition | Conference | ECVP | |||
| Notes | CIC; NEUROBIT; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ CPO2014 | Serial | 2527 | |||
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| Author | Ricard Balague |
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| 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 | |||
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| 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 | ||||
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| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Bal2014 | Serial | 2579 | |||
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| Author | C. Alejandro Parraga |
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| Title | Perceptual Psychophysics | Type | Book Chapter | |||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | ||
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Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | |||
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| ISSN | ISBN | 978-3-527-41264-8 | Medium | |||
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| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | |||
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| Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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| 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 | |
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| Address | Carnoustie; Scotland; March 2015 | |||||
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| Area | Expedition | Conference | SGV | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ OPC2015a | Serial | 2632 | |||
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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 | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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| 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 | ||
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| Address | Barcelona; June 2015 | |||||
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| Area | Expedition | Conference | BARCCSYN | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ OPC2015b | Serial | 2634 | |||
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| Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez |
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| 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 | |
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| 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. | |||||
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| Notes | CIC; 600.087 | Approved | no | |||
| Call Number | Admin @ si @ RVB2017 | Serial | 2892 | |||
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| Author | Jordi Roca |
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| Title | Constancy and inconstancy in categorical colour perception | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| 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. | |||||
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| Corporate Author | Thesis | Ph.D. thesis | ||||
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Place of Publication | Editor | Maria Vanrell;C. Alejandro Parraga | |||
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| Call Number | Admin @ si @ Roc2012 | Serial | 2893 | |||
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| Author | Ivet Rafegas; Maria Vanrell |
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| 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 | |||
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| Abstract | Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
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| Address | San Diego; USA; November 2016 | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RaV2016a | Serial | 2894 | |||
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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 | 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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