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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 | Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool | ||||
Title | Active MAP Inference in CRFs for Efficient Semantic Segmentation | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
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Issue | Pages | 2312 - 2319 | ||
Keywords | Semantic Segmentation | ||||
Abstract | Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. | ||||
Address | Sydney; Australia; December 2013 | ||||
Corporate Author | Thesis | ||||
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ISSN | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | ADAS; 600.057 | Approved | no | ||
Call Number | ADAS @ adas @ RBN2013 | Serial | 2377 | ||
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Author | Joan M. Nuñez; Debora Gil; Fernando Vilariño | ||||
Title | Finger joint characterization from X-ray images for rheymatoid arthritis assessment | Type | Conference Article | ||
Year | 2013 | Publication | 6th International Conference on Biomedical Electronics and Devices | Abbreviated Journal | |
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Issue | Pages | 288-292 | ||
Keywords | Rheumatoid Arthritis; X-Ray; Hand Joint; Sclerosis; Sharp Van der Heijde | ||||
Abstract | In this study we propose amodular systemfor automatic rheumatoid arthritis assessment which provides a joint space width measure. A hand joint model is proposed based on the accurate analysis of a X-ray finger joint image sample set. This model shows that the sclerosis and the lower bone are the main necessary features in order to perform a proper finger joint characterization. We propose sclerosis and lower bone detection methods as well as the experimental setup necessary for its performance assessment. Our characterization is used to propose and compute a joint space width score which is shown to be related to the different degrees of arthritis. This assertion is verified by comparing our proposed score with Sharp Van der Heijde score, confirming that the lower our score is the more advanced is the patient affection. | ||||
Address | Barcelona; February 2013 | ||||
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Publisher | SciTePress | Place of Publication | Editor | ||
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Area | 800 | Expedition | Conference | BIODEVICES | |
Notes | IAM;MV; 600.057; 600.054;SIAI | Approved | no | ||
Call Number | IAM @ iam @ NGV2013 | Serial | 2196 | ||
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Author | David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha | ||||
Title | On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts | Type | Conference Article | ||
Year | 2012 | Publication | 13th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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Issue | Pages | 763-768 | ||
Keywords | document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding | ||||
Abstract | he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20% | ||||
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ISSN | ISBN | 978-1-4673-2262-1 | Medium | ||
Area | Expedition | Conference | ICFHR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ FLF2012 | Serial | 2200 | ||
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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 | |
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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. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Mikhail Mozerov;Jordi Gonzalez | |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Ama2012 | Serial | 2201 | ||
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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 | |
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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. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Elf2012 | Serial | 2202 | ||
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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 | |
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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. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Ped2012 | Serial | 2203 | ||
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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 | |
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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. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny | |
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ Gib2012 | Serial | 2204 | ||
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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 | |
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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. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Rou2012 | Serial | 2205 | ||
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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 | |
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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 |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joan Serrat | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Rub2012 | Serial | 2206 | ||
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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 | |
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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 | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Cha2012 | Serial | 2207 | ||
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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 | |
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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 |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Theo Gevers | |
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ Gon2012 | Serial | 2208 | ||
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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 | |
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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. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Felipe Lumbreras;Angel Sappa | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Bar2012 | Serial | 2209 | ||
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Author | Diego Alejandro Cheda | ||||
Title | Monocular Depth Cues in Computer Vision Applications | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Depth perception is a key aspect of human vision. It is a routine and essential visual task that the human do effortlessly in many daily activities. This has often been associated with stereo vision, but humans have an amazing ability to perceive depth relations even from a single image by using several monocular cues.
In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. Nevertheless, given a single image, this possibility is usually discarded, since obtaining depth information has frequently been performed by three-dimensional reconstruction techniques, requiring two or more images of the same scene taken from different viewpoints. Recently, some proposals have shown the feasibility of computing depth information from single images. In essence, the idea is to take advantage of a priori knowledge of the acquisition conditions and the observed scene to estimate depth from monocular pictorial cues. These approaches try to precisely estimate the scene depth maps by employing computationally demanding techniques. However, to assist many computer vision algorithms, it is not really necessary computing a costly and detailed depth map of the image. Indeed, just a rough depth description can be very valuable in many problems. In this thesis, we have demonstrated how coarse depth information can be integrated in different tasks following alternative strategies to obtain more precise and robust results. In that sense, we have proposed a simple, but reliable enough technique, whereby image scene regions are categorized into discrete depth ranges to build a coarse depth map. Based on this representation, we have explored the potential usefulness of our method in three application domains from novel viewpoints: camera rotation parameters estimation, background estimation and pedestrian candidate generation. In the first case, we have computed camera rotation mounted in a moving vehicle applying two novels methods based on distant elements in the image, where the translation component of the image flow vectors is negligible. In background estimation, we have proposed a novel method to reconstruct the background by penalizing close regions in a cost function, which integrates color, motion, and depth terms. Finally, we have benefited of geometric and depth information available on single images for pedestrian candidate generation to significantly reduce the number of generated windows to be further processed by a pedestrian classifier. In all cases, results have shown that our approaches contribute to better performances. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Daniel Ponsa;Antonio Lopez | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Che2012 | Serial | 2210 | ||
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Author | Jorge Bernal | ||||
Title | Polyp Localization and Segmentation in Colonoscopy Images by Means of a Model of Appearance for Polyps | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Colorectal cancer is the fourth most common cause of cancer death worldwide and its survival rate depends on the stage in which it is detected on hence the necessity for an early colon screening. There are several screening techniques but colonoscopy is still nowadays the gold standard, although it has some drawbacks such as the miss rate. Our contribution, in the field of intelligent systems for colonoscopy, aims at providing a polyp localization and a polyp segmentation system based on a model of appearance for polyps. To develop both methods we define a model of appearance for polyps, which describes a polyp as enclosed by intensity valleys. The novelty of our contribution resides on the fact that we include in our model aspects of the image formation and we also consider the presence of other elements from the endoluminal scene such as specular highlights and blood vessels, which have an impact on the performance of our methods. In order to develop our polyp localization method we accumulate valley information in order to generate energy maps, which are also used to guide the polyp segmentation. Our methods achieve promising results in polyp localization and segmentation. As we want to explore the usability of our methods we present a comparative analysis between physicians fixations obtained via an eye tracking device and our polyp localization method. The results show that our method is indistinguishable to novice physicians although it is far from expert physicians. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | F. Javier Sanchez;Fernando Vilariño | |
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Area | 800 | Expedition | Conference | ||
Notes | MV | Approved | no | ||
Call Number | Admin @ si @ Ber2012 | Serial | 2211 | ||
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