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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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| 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 | Marina Alberti | ||||
| Title | Detection and Alignment of Vascular Structures in Intravascular Ultrasound using Pattern Recognition Techniques | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this thesis, several methods for the automatic analysis of Intravascular Ultrasound
(IVUS) sequences are presented, aimed at assisting physicians in the diagnosis, the assessment of the intervention and the monitoring of the patients with coronary disease. The basis for the developed frameworks are machine learning, pattern recognition and image processing techniques. First, a novel approach for the automatic detection of vascular bifurcations in IVUS is presented. The task is addressed as a binary classi cation problem (identifying bifurcation and non-bifurcation angular sectors in the sequence images). The multiscale stacked sequential learning algorithm is applied, to take into account the spatial and temporal context in IVUS sequences, and the results are re ned using a-priori information about branching dimensions and geometry. The achieved performance is comparable to intra- and inter-observer variability. Then, we propose a novel method for the automatic non-rigid alignment of IVUS sequences of the same patient, acquired at dierent moments (before and after percutaneous coronary intervention, or at baseline and follow-up examinations). The method is based on the description of the morphological content of the vessel, obtained by extracting temporal morphological pro les from the IVUS acquisitions, by means of methods for segmentation, characterization and detection in IVUS. A technique for non-rigid sequence alignment – the Dynamic Time Warping algorithm - is applied to the pro les and adapted to the speci c clinical problem. Two dierent robust strategies are proposed to address the partial overlapping between frames of corresponding sequences, and a regularization term is introduced to compensate for possible errors in the pro le extraction. The bene ts of the proposed strategy are demonstrated by extensive validation on synthetic and in-vivo data. The results show the interest of the proposed non-linear alignment and the clinical value of the method. Finally, a novel automatic approach for the extraction of the luminal border in IVUS images is presented. The method applies the multiscale stacked sequential learning algorithm and extends it to 2-D+T, in a rst classi cation phase (the identi- cation of lumen and non-lumen regions of the images), while an active contour model is used in a second phase, to identify the lumen contour. The method is extended to the longitudinal dimension of the sequences and it is validated on a challenging data-set. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Simone Balocco;Petia Radeva | |
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Alb2013 | Serial | 2215 | ||
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| Author | Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A | Type | Book Whole | ||
| Year | 2008 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2008b | Serial | 2217 | ||
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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 | Juan Diego Gomez | ||||
| Title | Toward Robust Myocardial Blush Grade Estimation in Contrast Angiography | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 134 | 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 | MILAB | Approved | no | ||
| Call Number | Admin @ si @ Gom2009 | Serial | 2393 | ||
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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 | Jaume Gibert | ||||
| Title | Learning structural representations and graph matching paradigms in the context of object recognition | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 143 | Issue | Pages |
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Gib2009 | Serial | 2397 | ||
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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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