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Author Xavier Otazu
Title Perceptual tone-mapping operator based on multiresolution contrast decomposition Type Abstract
Year 2012 Publication Perception Abbreviated Journal PER
Volume 41 Issue Pages 86
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0301-0066 ISBN Medium
Area Expedition Conference (up)
Notes CIC Approved no
Call Number Admin @ si @ Ota2012 Serial 2179
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Author Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu
Title Switching off brightness induction through induction-reversed images Type Abstract
Year 2012 Publication Perception Abbreviated Journal PER
Volume 41 Issue Pages 208
Keywords
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
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes CIC Approved no
Call Number Admin @ si @ PDO2012a Serial 2180
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Author Fadi Dornaika; Bogdan Raducanu
Title Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation Type Book Chapter
Year 2012 Publication Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions Abbreviated Journal
Volume Issue Pages 157-178
Keywords
Abstract
Address
Corporate Author Thesis
Publisher NOVA Publishers Place of Publication Editor S.E. Carter
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes OR;MV Approved no
Call Number Admin @ si @ DoR2012 Serial 2183
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Author Jose Manuel Alvarez; Antonio Lopez
Title Photometric Invariance by Machine Learning Type Book Chapter
Year 2012 Publication Color in Computer Vision: Fundamentals and Applications Abbreviated Journal
Volume 7 Issue Pages 113-134
Keywords road detection
Abstract
Address
Corporate Author Thesis
Publisher iConcept Press Ltd Place of Publication Editor Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-470-89084-4 Medium
Area Expedition Conference (up)
Notes ADAS Approved no
Call Number Admin @ si @ AlL2012 Serial 2186
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Author Jordi Roca; C. Alejandro Parraga; Maria Vanrell
Title Predicting categorical colour perception in successive colour constancy Type Abstract
Year 2012 Publication Perception Abbreviated Journal PER
Volume 41 Issue Pages 138
Keywords
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.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0301-0066 ISBN Medium
Area Expedition Conference (up)
Notes CIC Approved no
Call Number Admin @ si @ RPV2012 Serial 2188
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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
Volume Issue Pages
Keywords
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
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Maria Vanrell;Xavier Otazu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes CIC Approved no
Call Number Alv2012b Serial 2216
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Author Ariel Amato
Title Environment-Independent Moving Cast Shadow Suppression in Video Surveillance Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This thesis is devoted to moving shadows detection and suppression. Shadows could be defined as the parts of the scene that are not directly illuminated by a light source due to obstructing object or objects. Often, moving shadows in images sequences are undesirable since they could cause degradation of the expected results during processing of images for object detection, segmentation, scene surveillance or similar purposes. In this thesis first moving shadow detection methods are exhaustively overviewed. Beside the mentioned methods from literature and to compensate their limitations a new moving shadow detection method is proposed. It requires no prior knowledge about the scene, nor is it restricted to assumptions about specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene the values of the background image are divided by values of the current frame in the RGB color space. In the thesis how this luminance ratio can be used to identify segments with low gradient constancy is shown, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of the proposed method compared with the most sophisticated state-of-the-art shadow detection algorithms. These results show that the proposed approach is robust and accurate over a broad range of shadow types and challenging video conditions.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Mikhail Mozerov;Jordi Gonzalez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ISE Approved no
Call Number Admin @ si @ Ama2012 Serial 2201
Permanent link to this record
 
 
Author Noha Elfiky
Title Compact, Adaptive and Discriminative Spatial Pyramids for Improved Object and Scene Classification Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The release of challenging datasets with a vast number of images, requires the development of efficient image representations and algorithms which are able to manipulate these large-scale datasets efficiently. Nowadays the Bag-of-Words (BoW) is the most successful approach in the context of object and scene classification tasks. However, its main drawback is the absence of the important spatial information. Spatial pyramids (SP) have been successfully applied to incorporate spatial information into BoW-based image representation. Observing the remarkable performance of spatial pyramids, their growing number of applications to a broad range of vision problems, and finally its geometry inclusion, a question can be asked what are the limits of spatial pyramids. Within the SP framework, the optimal way for obtaining an image spatial representation, which is able to cope with it’s most foremost shortcomings, concretely, it’s high dimensionality and the rigidity of the resulting image representation, still remains an active research domain. In summary, the main concern of this thesis is to search for the limits of spatial pyramids and try to figure out solutions for them.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ISE Approved no
Call Number Admin @ si @ Elf2012 Serial 2202
Permanent link to this record
 
 
Author Marco Pedersoli
Title Hierarchical Multiresolution Models for fast Object Detection Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The ability to automatically detect and recognize objects in unconstrained images is becoming more and more critical: from security systems and autonomous robots, to smart phones and augmented reality, intelligent devices need to understand the meaning of images as a composition of semantic objects. This Thesis tackles the problem of fast object detection based on template models. Detection consists of searching for an object in an image by evaluating the similarity between a template model and an image region at each possible location and scale. In this work, we show that using a template model representation based on a multiple resolution hierarchy is an optimal choice that can lead to excellent detection accuracy and fast computation. We implement two different approaches that make use of a hierarchy of multiresolution models: a multiresolution cascade and a coarse-to-fine search. Also, we extend the coarse-to-fine search by introducing a deformable part-based model that achieves state-of-the-art results together with a very reduced computational cost. Finally, we specialize our approach to the challenging task of pedestrian detection from moving vehicles and show that the overall quality of the system outperforms previous works in terms of speed and accuracy.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ISE Approved no
Call Number Admin @ si @ Ped2012 Serial 2203
Permanent link to this record
 
 
Author Jaume Gibert
Title Vector Space Embedding of Graphs via Statistics of Labelling Information Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.

Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.

In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Ernest Valveny
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes DAG Approved no
Call Number Admin @ si @ Gib2012 Serial 2204
Permanent link to this record
 
 
Author Mohammad Rouhani
Title Shape Representation and Registration using Implicit Functions Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Shape representation and registration are two important problems in computer vision and graphics. Representing the given cloud of points through an implicit function provides a higher level information describing the data. This representation can be more compact more robust to noise and outliers, hence it can be exploited in different computer vision application. In the first part of this thesis implicit shape representations, including both implicit B-spline and polynomial, are tackled. First, an approximation of a geometric distance is proposed to measure the closeness of the given cloud of points and the implicit surface. The analysis of the proposed distance shows an accurate estimation with smooth behavior. The distance by itself is used in a RANSAC based quadratic fitting method. Moreover, since the gradient information of the distance with respect to the surface parameters can be analytically computed, it is used in Levenberg-Marquadt algorithm to refine the surface parameters. In a different approach, an algebraic fitting method is used to represent an object through implicit B-splines. The outcome is a smooth flexible surface and can be represented in different levels from coarse to fine. This property has been exploited to solve the registration problem in the second part of the thesis. In the proposed registration technique the model set is replaced with an implicit representation provided in the first part; then, the point-to-point registration is converted to a point-to-model one in a higher level. This registration error can benefit from different distance estimations to speed up the registration process even without need of correspondence search. Finally, the non-rigid registration problem is tackled through a quadratic distance approximation that is based on the curvature information of the model set. This approximation is used in a free form deformation model to update its control lattice. Then it is shown how an accurate distance approximation can benefit non-rigid registration problems.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ADAS Approved no
Call Number Admin @ si @ Rou2012 Serial 2205
Permanent link to this record
 
 
Author Jose Carlos Rubio
Title Many-to-Many High Order Matching. Applications to Tracking and Object Segmentation Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Feature matching is a fundamental problem in Computer Vision, having multiple applications such as tracking, image classification and retrieval, shape recognition and stereo fusion. In numerous domains, it is useful to represent the local structure of the matching features to increase the matching accuracy or to make the correspondence invariant to certain transformations (affine, homography, etc. . . ). However, encoding this knowledge requires complicating the model by establishing high-order relationships between the model elements, and therefore increasing the complexity of the optimization problem.

The importance of many-to-many matching is sometimes dismissed in the literature. Most methods are restricted to perform one-to-one matching, and are usually validated on synthetic, or non-realistic datasets. In a real challenging environment, with scale, pose and illumination variations of the object of interest, as well as the presence of occlusions, clutter, and noisy observations, many-to-many matching is necessary to achieve satisfactory results. As a consequence, finding the most likely many-to-many correspondence often involves a challenging combinatorial optimization process.

In this work, we design and demonstrate matching algorithms that compute many-to-many correspondences, applied to several challenging problems. Our goal is to make use of high-order representations to improve the expressive power of the matching, at the same time that we make feasible the process of inference or optimization of such models. We effectively use graphical models as our preferred representation because they provide an elegant probabilistic framework to tackle structured prediction problems.

We introduce a matching-based tracking algorithm which performs matching between frames of a video sequence in order to solve the difficult problem of headlight tracking at night-time. We also generalise this algorithm to solve the problem of data association applied to various tracking scenarios. We demonstrate the effectiveness of such approach in real video sequences and we show that our tracking algorithm can be used to improve the accuracy of a headlight classification system.

In the second part of this work, we move from single (point) matching to dense (region) matching and we introduce a new hierarchical image representation. We make use of such model to develop a high-order many-to-many matching between pairs of images. We show that the use of high-order models in comparison to simpler models improves not only the accuracy of the results, but also the convergence speed of the inference algorithm.

Finally, we keep exploiting the idea of region matching to design a fully unsupervised image co-segmentation algorithm that is able to perform competitively with state-of-the-art supervised methods. Our method also overcomes the typical drawbacks of some of the past works, such as avoiding the necessity of variate appearances on the image backgrounds. The region matching in this case is applied to effectively exploit inter-image information. We also extend this work to perform co-segmentation of videos, being the first time that such problem is addressed, as a way to perform video object segmentation
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joan Serrat
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ADAS Approved no
Call Number Admin @ si @ Rub2012 Serial 2206
Permanent link to this record
 
 
Author Bhaskar Chakraborty
Title Model free approach to human action recognition Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic understanding of human activity and action is very important and challenging research area of Computer Vision with wide applications in video surveillance, motion analysis, virtual reality interfaces, video indexing, content based video retrieval, HCI and health care. This thesis presents a series of techniques to solve the problem of human action recognition in video. First approach towards this goal is based on a probabilistic optimization model of body parts using Hidden Markov Model. This strong model based approach is able to distinguish between similar actions by only considering the body parts having major contributions to the actions. In next approach, we apply a weak model based human detector and actions are represented by Bag-of-key poses model to capture the human pose changes during the actions. To tackle the problem of human action recognition in complex scenes, a selective spatio-temporal interest point (STIP) detector is proposed by using a mechanism similar to that of the non-classical receptive field inhibition that is exhibited by most oriented selective neuron in the primary visual cortex. An extension of the selective STIP detector is applied to multi-view action recognition system by introducing a novel 4D STIPs (3D space + time). Finally, we use our STIP detector on large scale continuous visual event recognition problem and propose a novel generalized max-margin Hough transformation framework for activity detection
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ISE Approved no
Call Number Admin @ si @ Cha2012 Serial 2207
Permanent link to this record
 
 
Author Josep M. Gonfaus
Title Towards Deep Image Understanding: From pixels to semantics Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity.
Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Theo Gevers
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ISE Approved no
Call Number Admin @ si @ Gon2012 Serial 2208
Permanent link to this record
 
 
Author Fernando Barrera
Title Multimodal Stereo from Thermal Infrared and Visible Spectrum Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recent advances in thermal infrared imaging (LWIR) has allowed its use in applications beyond of the military domain. Nowadays, this new family of sensors is included in different technical and scientific applications. They offer features that facilitate tasks, such as detection of pedestrians, hot spots, differences in temperature, among others, which can significantly improve the performance of a system where the persons are expected to play the principal role. For instance, video surveillance applications, monitoring, and pedestrian detection.
During this dissertation the next question is stated: Could a couple of sensors measuring different bands of the electromagnetic spectrum, as the visible and thermal infrared, be used to extract depth information? Although it is a complex question, we shows that a system of these characteristics is possible as well as their advantages, drawbacks, and potential opportunities.
The matching and fusion of data coming from different sensors, as the emissions registered at visible and infrared bands, represents a special challenge, because it has been showed that theses signals are weak correlated. Therefore, many traditional techniques of image processing and computer vision are not helpful, requiring adjustments for their correct performance in every modality.
In this research an experimental study that compares different cost functions and matching approaches is performed, in order to build a multimodal stereovision system. Furthermore, the common problems in infrared/visible stereo, specially in the outdoor scenes are identified. Our framework summarizes the architecture of a generic stereo algorithm, at different levels: computational, functional, and structural, which can be extended toward high-level fusion (semantic) and high-order (prior).The proposed framework is intended to explore novel multimodal stereo matching approaches, going from sparse to dense representations (both disparity and depth maps). Moreover, context information is added in form of priors and assumptions. Finally, this dissertation shows a promissory way toward the integration of multiple sensors for recovering three-dimensional information.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Felipe Lumbreras;Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference (up)
Notes ADAS Approved no
Call Number Admin @ si @ Bar2012 Serial 2209
Permanent link to this record