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Author | Xavier Otazu |
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Title | Perceptual tone-mapping operator based on multiresolution contrast decomposition | Type | Abstract | |||
Year | 2012 | Publication | Perception | Abbreviated Journal | PER | |
Volume | 41 | Issue | Pages | 86 | ||
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Abstract | Tone-mapping operators (TMO) are used to display high dynamic range(HDR) images in low dynamic range (LDR) displays. Many computational and biologically inspired approaches have been used in the literature, being many of them based on multiresolution decompositions. In this work, a simple two stage model for TMO is presented. The first stage is a novel multiresolution contrast decomposition, which is inspired in a pyramidal contrast decomposition (Peli, 1990 Journal of the Optical Society of America7(10), 2032-2040).
This novel multiresolution decomposition represents the Michelson contrast of the image at different spatial scales. This multiresolution contrast representation, applied on the intensity channel of an opponent colour decomposition, is processed by a non-linear saturating model of V1 neurons (Albrecht et al, 2002 Journal ofNeurophysiology 88(2) 888-913). This saturation model depends on the visual frequency, and it has been modified in order to include information from the extended Contrast Sensitivity Function (e-CSF) (Otazu et al, 2010 Journal ofVision10(12) 5). A set of HDR images in Radiance RGBE format (from CIS HDR Photographic Survey and Greg Ward database) have been used to test the model, obtaining a set of LDR images. The resulting LDR images do not show the usual halo or color modification artifacts. |
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ISSN | 0301-0066 | ISBN ![]() |
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ Ota2012 | Serial | 2179 | |||
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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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Call Number | Admin @ si @ PDO2012a | Serial | 2180 | |||
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Author | Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu |
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Title | A Neurodynamical Model Of Brightness Induction In V1 Following Static And Dynamic Contextual Influences | Type | Abstract | |||
Year | 2012 | Publication | 8th Federation of European Neurosciences | Abbreviated Journal | ||
Volume | 6 | Issue | Pages | 63-64 | ||
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Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although striate cortex is traditionally regarded as an area mostly responsive to ensory (i.e. retinal) information,
neurophysiological evidence suggests that perceived brightness information mightbe explicitly represented in V1. Such evidence has been observed both in anesthetised cats where neuronal response modulations have been found to follow luminance changes outside the receptive felds and in human fMRI measurements. In this work, possible neural mechanisms that ofer a plausible explanation for such phenomenon are investigated. To this end, we consider the model proposed by Z.Li (Li, Network:Comput. Neural Syst., 10 (1999)) which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual infuences, i.e. layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has reproduced other phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant efect of contextual infuences. We have extended the original model such that the input to the network is obtained from a complete multiscale and multiorientation wavelet decomposition, thereby allowing the recovery of an image refecting the perceived intensity. The proposed model successfully accounts for well known psychophysical efects for static contexts (among them: the White's and modifed White's efects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction efects) and also for brigthness induction in dynamic contexts defned by modulating the luminance of surrounding areas (e.g. the brightness of a static central area is perceived to vary in antiphase to the sinusoidal luminance changes of its surroundings). This work thus suggests that intra-cortical interactions in V1 could partially explain perceptual brightness induction efects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual processing pathway. |
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Area | Expedition | Conference | FENS | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ PDO2012b | Serial | 2181 | |||
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Author | Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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Title | Predicting categorical colour perception in successive colour constancy | Type | Abstract | |||
Year | 2012 | Publication | Perception | Abbreviated Journal | PER | |
Volume | 41 | Issue | Pages | 138 | ||
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Abstract | Colour constancy is a perceptual mechanism that seeks to keep the colour of objects relatively stable under an illumination shift. Experiments haveshown that its effects depend on the number of colours present in the scene. We
studied categorical colour changes under different adaptation states, in particular, whether the colour categories seen under a chromatically neutral illuminant are the same after a shift in the chromaticity of the illumination. To do this, we developed the chromatic setting paradigm (2011 Journal of Vision11 349), which is as an extension of achromatic setting to colour categories. The paradigm exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. Our experiments were run on a CRT monitor (inside a dark room) under various simulated illuminants and restricting the number of colours of the Mondrian background to three, thus weakening the adaptation effect. Our results show a change in the colour categories present before (under neutral illumination) and after adaptation (under coloured illuminants) with a tendency for adapted colours to be less saturated than before adaptation. This behaviour was predicted by a simple affine matrix model, adjusted to the chromatic setting results. |
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ISSN | 0301-0066 | ISBN ![]() |
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RPV2012 | Serial | 2188 | |||
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Author | Susana Alvarez |
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Title | 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 |
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Title | Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective | Type | Book Whole | |||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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Abstract | In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks. Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification. As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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Corporate Author | Thesis | Ph.D. thesis | ||||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu;Maria Vanrell | ||
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Call Number | Admin @ si @ Mur2012 | Serial | 2212 | |||
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Author | David Augusto Rojas |
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Title | Colouring Local Feature Detection for Matching | Type | Report | |||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | ||
Volume | 133 | 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 @ Roj2009 | Serial | 2392 | |||
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Author | Olivier Penacchio |
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Title | 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 | Xavier Boix |
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Title | Learning Conditional Random Fields for Stereo | Type | Report | |||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | ||
Volume | 136 | 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 @ Boi2009 | Serial | 2395 | |||
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Author | Shida Beigpour |
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Title | Physics-based Reflectance Estimation Applied to Recoloring | Type | Report | |||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | ||
Volume | 137 | 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 @ Bei2009 | Serial | 2396 | |||
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Author | Jose Carlos Rubio |
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Title | Graph matching based on graphical models with application to vehicle tracking and classification at night | Type | Report | |||
Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | ||
Volume | 144 | 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 @ Rub2009 | Serial | 2398 | |||
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Author | Ivet Rafegas |
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Title | Exploring Low-Level Vision Models. Case Study: Saliency Prediction | Type | Report | |||
Year | 2013 | Publication | CVC Technical Report | Abbreviated Journal | ||
Volume | 175 | Issue | Pages | |||
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Corporate Author | Thesis | Master's thesis | ||||
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ Raf2013 | Serial | 2409 | |||
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Author | Adria 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 ![]() |
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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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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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Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
Call Number | Admin @ si @ DKF2014 | Serial | 2509 | |||
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