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| Author | Jordi Vitria; Petia Radeva; X. Binefa; A. Pujol; Ernest Valveny; Robert Benavente; Craig Von Land |
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Real time recognition of pharmaceutical products by subspace methods | Type | Report | |||
| Year | 1999 | Publication | CVC Technical Report #35 | Abbreviated Journal | ||
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| Address | CVC (UAB) | |||||
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| Notes | OR;MILAB;DAG;CIC;MV | Approved | no | |||
| Call Number | BCNPCL @ bcnpcl @ VRB1999b | Serial | 54 | |||
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| Author | X. Binefa; Jordi Vitria; Maria Vanrell |
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Reconstruccion tridimensional de imagenes Microscopicas. | Type | Miscellaneous | |||
| Year | 1992 | Publication | V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes | Abbreviated Journal | ||
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| Notes | OR;CIC;MV | Approved | no | |||
| Call Number | BCNPCL @ bcnpcl @ BVV1992b | Serial | 255 | |||
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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 | Olivier Penacchio |
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Relative Density of L, M, S photoreceptors in the Human Retina | Type | Report | |||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | ||
| Volume | 135 | Issue | Pages | |||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Pen2009 | Serial | 2394 | |||
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| Author | Francesc Tous; Maria Vanrell; Ramon Baldrich |
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| Title |
Relaxed Grey-World: Computational Colour Constancy by Surface Matching | Type | Book Chapter | |||
| Year | 2005 | Publication | Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522:192–199 | Abbreviated Journal | ||
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| Address | Estoril (Portugal) | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ TVB2005 | Serial | 555 | |||
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| Author | C. Alejandro Parraga; Olivier Penacchio; Maria Vanrell |
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| Title |
Retinal Filtering Matches Natural Image Statistics at Low Luminance Levels | Type | Journal Article | |||
| Year | 2011 | Publication | Perception | Abbreviated Journal | PER | |
| Volume | 40 | Issue | Pages | 96 | ||
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| Abstract | The assumption that the retina’s main objective is to provide a minimum entropy representation to higher visual areas (ie efficient coding principle) allows to predict retinal filtering in space–time and colour (Atick, 1992 Network 3 213–251). This is achieved by considering the power spectra of natural images (which is proportional to 1/f2) and the suppression of retinal and image noise. However, most studies consider images within a limited range of lighting conditions (eg near noon) whereas the visual system’s spatial filtering depends on light intensity and the spatiochromatic properties of natural scenes depend of the time of the day. Here, we explore whether the dependence of visual spatial filtering on luminance match the changes in power spectrum of natural scenes at different times of the day. Using human cone-activation based naturalistic stimuli (from the Barcelona Calibrated Images Database), we show that for a range of luminance levels, the shape of the retinal CSF reflects the slope of the power spectrum at low spatial frequencies. Accordingly, the retina implements the filtering which best decorrelates the input signal at every luminance level. This result is in line with the body of work that places efficient coding as a guiding neural principle. | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ PPV2011 | Serial | 1720 | |||
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| Author | Susana Alvarez |
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Revisión de la teoría de los Textons Enfoque computacional en color | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | El color y la textura son dos estímulos visuales importantes para la interpretación de las imágenes. La definición de descriptores computacionales que combinan estas dos características es aún un problema abierto. La dificultad se deriva esencialmente de la propia naturaleza de ambas, mientras que la textura es una propiedad de una región, el color es una propiedad de un punto.
Hasta ahora se han utilizado tres los tipos de aproximaciones para la combinación, (a) se describe la textura directamente en cada uno de los canales color, (b) se describen textura y color por separado y se combinan al final, y (c) la combinación se realiza con técnicas de aprendizaje automático. Considerando que este problema se resuelve en el sistema visual humano en niveles muy tempranos, en esta tesis se propone estudiar el problema a partir de la implementación directa de una teoría perceptual, la teoría de los textons, y explorar así su extensión a color. Puesto que la teoría de los textons se basa en la descripción de la textura a partir de las densidades de los atributos locales, esto se adapta perfectamente al marco de trabajo de los descriptores holísticos (bag-of-words). Se han estudiado diversos descriptores basados en diferentes espacios de textons, y diferentes representaciones de las imágenes. Asimismo se ha estudiado la viabilidad de estos descriptores en una representación conceptual de nivel intermedio. Los descriptores propuestos han demostrado ser muy eficientes en aplicaciones de recuperación y clasificación de imágenes, presentando ventajas en la generación de vocabularios. Los vocabularios se obtienen cuantificando directamente espacios de baja dimensión y la perceptualidad de estos espacios permite asociar semántica de bajo nivel a las palabras visuales. El estudio de los resultados permite concluir que si bien la aproximación holística es muy eficiente, la introducción de co-ocurrencia espacial de las propiedades de forma y color de los blobs de la imagen es un elemento clave para su combinación, hecho que no contradice las evidencias en percepción |
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell;Xavier Otazu | ||
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| Notes | CIC | Approved | no | |||
| Call Number | Alv2012b | Serial | 2216 | |||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
 
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| Title |
Saliency Estimation Using a Non-Parametric Low-Level Vision Model | Type | Conference Article | |||
| Year | 2011 | Publication | IEEE conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 433-440 | |||
| Keywords | Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms | |||||
| Abstract | Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. | |||||
| Address | Colorado Springs | |||||
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| ISSN | 1063-6919 | ISBN | 978-1-4577-0394-2 | Medium | ||
| Area | Expedition | Conference | CVPR | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MVO2011 | Serial | 1757 | |||
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| Author | Eduard Vazquez; Theo Gevers; M. Lucassen; Joost Van de Weijer; Ramon Baldrich |
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| Title |
Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception | Type | Journal Article | |||
| Year | 2010 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A | |
| Volume | 27 | Issue | 3 | Pages | 613–621 | |
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| Abstract | In this paper, computational methods are proposed to compute color edge saliency based on the information content of color edges. The computational methods are evaluated on bottom-up saliency in a psychophysical experiment, and on a more complex task of salient object detection in real-world images. The psychophysical experiment demonstrates the relevance of using information theory as a saliency processing model and that the proposed methods are significantly better in predicting color saliency (with a human-method correspondence up to 74.75% and an observer agreement of 86.8%) than state-of-the-art models. Furthermore, results from salient object detection confirm that an early fusion of color and contrast provide accurate performance to compute visual saliency with a hit rate up to 95.2%. | |||||
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| Notes | ISE;CIC | Approved | no | |||
| Call Number | CAT @ cat @ VGL2010 | Serial | 1275 | |||
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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 | 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 | Xavier Otazu; Maria Vanrell |
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| Title |
Several lightness induction effects with a computational multiresolution wavelet framework | Type | Journal | |||
| Year | 2006 | Publication | 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56 | Abbreviated Journal | ||
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| Address | Saint-Petersburg (Russia) | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ OtV2006 | Serial | 659 | |||
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| Author | Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Shadow Resistant Road Segmentation from a Mobile Monocular System | Type | Conference Article | |||
| Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 | Abbreviated Journal | ||
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| Keywords | road detection | |||||
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| Address | Gerona (Spain) | |||||
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| Notes | ADAS;CIC | Approved | no | |||
| Call Number | ADAS @ adas @ ALB2007 | Serial | 943 | |||
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| Author | Alicia Fornes; Xavier Otazu; Josep Llados |
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Show through cancellation and image enhancement by multiresolution contrast processing | Type | Conference Article | |||
| Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 200-204 | |||
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| Abstract | Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities. | |||||
| Address | Washington; USA; August 2013 | |||||
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| ISSN | 1520-5363 | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | |||
| Notes | DAG; 602.006; 600.045; 600.061; 600.052;CIC | Approved | no | |||
| Call Number | Admin @ si @ FOL2013 | Serial | 2241 | |||
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| Author | Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell |
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Spectral sharpening by spherical sampling | Type | Journal Article | |||
| Year | 2012 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A | |
| Volume | 29 | Issue | 7 | Pages | 1199-1210 | |
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| Abstract | There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art. | |||||
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| ISSN | 1084-7529 | ISBN | Medium | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ FVS2012 | Serial | 2000 | |||
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| Author | Robert Benavente; Francesc Tous; Ramon Baldrich; Maria Vanrell |
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Statical Modelling of a Colour Naming Space. | Type | Miscellaneous | |||
| Year | 2002 | Publication | Proceedings of the 1st. European Conference on Colour in Graphics Imaging and Vision: 406–411. | Abbreviated Journal | ||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ BTB2002 | Serial | 289 | |||
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| Author | Anna Salvatella; Maria Vanrell; Ramon Baldrich |
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Subtexture Components for Texture Description | Type | Miscellaneous | |||
| Year | 2003 | Publication | Lecture Notes in Computer Science, vol 2652, pp 884–892 | Abbreviated Journal | ||
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| Address | Springer-Verlag | |||||
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| Notes | CIC | Approved | no | |||
| Call Number | CAT @ cat @ SVR2003 | Serial | 421 | |||
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| Author | Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu |
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| Title |
Switching off brightness induction through induction-reversed images | Type | Abstract | |||
| Year | 2012 | Publication | Perception | Abbreviated Journal | PER | |
| Volume | 41 | Issue | Pages | 208 | ||
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| Abstract | Brightness induction is the modulation of the perceived intensity of an
area by the luminance of surrounding areas. Although V1 is traditionally regarded as an area mostly responsive to retinal information, neurophysiological evidence suggests that it may explicitly represent brightness information. In this work, we investigate possible neural mechanisms underlying brightness induction. To this end, we consider the model by Z Li (1999 Computation and Neural Systems10187-212) which is constrained by neurophysiological data and focuses on the part of V1 responsible for contextual influences. This model, which has proven to account for phenomena such as contour detection and preattentive segmentation, shares with brightness induction the relevant effect of contextual influences. Importantly, the input to our network model derives from a complete multiscale and multiorientation wavelet decomposition, which makes it possible to recover an image reflecting the perceived luminance and successfully accounts for well known psychophysical effects for both static and dynamic contexts. By further considering inverse problem techniques we define induction-reversed images: given a target image, we build an image whose perceived luminance matches the actual luminance of the original stimulus, thus effectively canceling out brightness induction effects. We suggest that induction-reversed images may help remove undesired perceptual effects and can find potential applications in fields such as radiological image interpretation |
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ PDO2012a | Serial | 2180 | |||
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