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Author | Susana Alvarez | ||||
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 | ||||
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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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Mur2012 | Serial | 2212 | ||
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Author | David Augusto Rojas | ||||
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 | ||||
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 | ||||
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 | ||||
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 | ||||
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 | ||||
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 | ||||
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 | ||||
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 | ||||
Corporate Author | Thesis | ||||
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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 | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras | ||||
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 | ||||
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 | ||||
Title | Color Vision, Computational Methods for | Type | Book Chapter | ||
Year | 2014 | Publication | Encyclopedia of Computational Neuroscience | Abbreviated Journal | |
Volume | Issue | Pages | 1-11 | ||
Keywords | Color computational vision; Computational neuroscience of color | ||||
Abstract | The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments. | ||||
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Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Editor | Dieter Jaeger; Ranu Jung | |
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-1-4614-7320-6 | Medium | ||
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Notes | CIC; 600.074 | Approved | no | ||
Call Number | Admin @ si @ Par2014 | Serial | 2512 | ||
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Author | Ricard Balague | ||||
Title | Exploring the combination of color cues for intrinsic image decomposition | Type | Report | ||
Year | 2014 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 178 | Issue | Pages | ||
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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 | ||||
Title | Perceptual Psychophysics | Type | Book Chapter | ||
Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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Publisher | Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | ||
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Series Volume | Series Issue | Edition | |||
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 | Marc Serra | ||||
Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | ||
Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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Address | September 2015 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Robert Benavente;Olivier Penacchio | |
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ISSN | ISBN | 978-84-943427-4-5 | Medium | ||
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Notes | CIC; 600.074 | Approved | no | ||
Call Number | Admin @ si @ Ser2015 | Serial | 2688 | ||
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Author | Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich | ||||
Title | DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition | Type | Conference Article | ||
Year | 2015 | Publication | Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II | Abbreviated Journal | |
Volume | 9475 | Issue | Pages | 463-473 | |
Keywords | Projector-camera systems; Feature descriptors; Object recognition | ||||
Abstract | Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. | ||||
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Publisher | Springer International Publishing | Place of Publication | Editor | ||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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ISSN | 0302-9743 | ISBN | 978-3-319-27862-9 | Medium | |
Area | Expedition | Conference | ISVC | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ SMG2015 | Serial | 2736 | ||
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Author | Jordi Roca | ||||
Title | Constancy and inconstancy in categorical colour perception | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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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 | |||
Publisher | Place of Publication | Editor | Maria Vanrell;C. Alejandro Parraga | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Roc2012 | Serial | 2893 | ||
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