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Author |
Pierluigi Casale |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Social Environment Description from Data Collected with a Wearable Device |
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Miscellaneous |
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Year |
2008 |
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CVC Technical Report #124 |
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Barcelona, Spain |
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no |
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Admin @ si @ Cas2008 |
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1151 |
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Author |
Nicola Bellotto; Eric Sommerlade; Ben Benfold; Charles Bibby; I. Reid; Daniel Roth; Luc Van Gool; Carles Fernandez; Jordi Gonzalez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Distributed Camera System for Multi-Resolution Surveillance |
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Conference Article |
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Year |
2009 |
Publication |
3rd ACM/IEEE International Conference on Distributed Smart Cameras |
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10.1109/ICDSC.2009.5289413 |
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We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance. |
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Como, Italy |
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ISE @ ise @ BSB2009 |
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1205 |
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Author |
Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
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Title |
Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences |
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Journal Article |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. |
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1110-8657 |
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ISE @ ise @ MRR2010 |
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1208 |
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Author |
Pau Baiget |
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Title |
Modeling Human Behavior for Image Sequence Understanding and Generation |
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Book Whole |
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Year |
2009 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The comprehension of animal behavior, especially human behavior, is one of the most ancient and studied problems since the beginning of civilization. The big list of factors that interact to determine a person action require the collaboration of different disciplines, such as psichology, biology, or sociology. In the last years the analysis of human behavior has received great attention also from the computer vision community, given the latest advances in the acquisition of human motion data from image sequences.
Despite the increasing availability of that data, there still exists a gap towards obtaining a conceptual representation of the obtained observations. Human behavior analysis is based on a qualitative interpretation of the results, and therefore the assignment of concepts to quantitative data is linked to a certain ambiguity.
This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents.
First, we propose methods to automatically learn the models from observations. Given a set of quantitative results output by a vision system, a normal behavior model is learnt. This results provides a tool to determine the normality or abnormality of future observations. However, machine learning methods are unable to provide a richer description of the observations. We confront this problem by means of a new method that incorporates prior knowledge about the enviornment and about the expected behaviors. This framework, formed by the reasoning engine FMTL and the modeling tool SGT allows the generation of conceptual descriptions of activity in new image sequences. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences.
The set of approaches presented in this Thesis has a growing set of potential applications. The analysis and description of behavior in image sequences has its principal application in the domain of smart video--surveillance, in order to detect suspicious or dangerous behaviors. Other applications include automatic sport commentaries, elderly monitoring, road traffic analysis, and the development of semantic video search engines. Alternatively, behavioral virtual agents allow to simulate accurate real situations, such as fires or crowds. Moreover, the inclusion of virtual agents into real image sequences has been widely deployed in the games and cinema industries. |
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Bellaterra (Spain) |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Jordi Gonzalez;Xavier Roca |
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Call Number |
Admin @ si @ Bai2009 |
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1210 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Juan J. Villanueva |
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Title |
High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients |
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Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of the detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a Support Vector Machine (SVM) composed by features at different resolution, from coarse for the first level to fine for the last one.
Considering that the spatial stride of the sliding window search is affected by the HOG features size, unlike previous methods based on Adaboost cascades, we can adopt a spatial stride inversely proportional to the features resolution. This produces that the speed-up of the cascade is not only due to the low number of features that need to be computed in the first levels, but also to the lower number of detection windows that needs to be evaluated.
Experimental results shows that our method permits a detection rate comparable with the state of the art, but at the same time a gain in the speed of the detection search of 10-20 times depending on the cascade configuration. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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ISE |
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no |
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Call Number |
ISE @ ise @ PGV2009 |
Serial |
1214 |
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Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez |
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Title |
Towards Real-Time Human Action Recognition |
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Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art. |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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ISE |
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DAG @ dag @ CBG2009 |
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1215 |
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Author |
Murad Al Haj; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
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Title |
Robust and Efficient Multipose Face Detection Using Skin Color Segmentation |
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Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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In this paper we describe an efficient technique for detecting faces in arbitrary images and video sequences. The approach is based on segmentation of images or video frames into skin-colored blobs using a pixel-based heuristic. Scale and translation invariant features are then computed from these segmented blobs which are used to perform statistical discrimination between face and non-face classes. We train and evaluate our method on a standard, publicly available database of face images and analyze its performance over a range of statistical pattern classifiers. The generalization of our approach is illustrated by testing on an independent sequence of frames containing many faces and non-faces. These experiments indicate that our proposed approach obtains false positive rates comparable to more complex, state-of-the-art techniques, and that it generalizes better to new data. Furthermore, the use of skin blobs and invariant features requires fewer training samples since significantly fewer non-face candidate regions must be considered when compared to AdaBoost-based approaches. |
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Springer Berlin Heidelberg |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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ISE |
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DAG @ dag @ ABG2009 |
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1216 |
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Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Title |
ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences |
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Conference Article |
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Year |
2009 |
Publication |
12th International Conference on Medical Image and Computer Assisted Intervention |
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Volume |
5762 |
Issue |
II |
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The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers. |
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London, UK |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04270-6 |
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MICCAI |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ CPF2009 |
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1228 |
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Author |
Gemma Roig; Xavier Boix; Fernando De la Torre |
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Title |
Optimal Feature Selection for Subspace Image Matching |
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Conference Article |
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Year |
2009 |
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2nd IEEE International Workshop on Subspace Methods in conjunction |
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Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features. |
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Kyoto, Japan |
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ICCV |
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no |
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Admin @ si @ RBT2009 |
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1233 |
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Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
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Title |
Prior Knowledge Based Motion Model Representation |
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Book Chapter |
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Year |
2009 |
Publication |
Progress in Computer Vision and Image Analysis |
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16 |
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Horst Bunke; JuanJose Villanueva; Gemma Sanchez |
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ADAS |
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no |
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ADAS @ adas @ SAM2009 |
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1235 |
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Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
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Title |
A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information |
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Conference Article |
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2009 |
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5th International Conference on Computer Vision Theory and Applications |
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Lisboa, Portugal |
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978-989-8111-69-2 |
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VISAPP |
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DAG |
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no |
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DAG @ dag @ RLP2009 |
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1238 |
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Author |
Dena Bazazian |
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Title |
Fully Convolutional Networks for Text Understanding in Scene Images |
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Book Whole |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging. |
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November 2018 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Andrew Bagdanov |
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978-84-948531-1-1 |
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DAG; 600.121 |
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Admin @ si @ Baz2018 |
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3220 |
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Eduard Vazquez |
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Distribution Characterization using Topological Features. Application to Colour Image Processing |
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2007 |
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CVC Technical Report # 107 |
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Admin @ si @ Vaz2009 |
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1254 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach |
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2009 |
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IEEE Workshop on Applications of Computer Vision |
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This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach. |
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Utah, USA |
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1550-5790 |
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978-1-4244-5497-6 |
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WACV |
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BCNPCL @ bcnpcl @ DoR2009b |
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David Rotger |
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Analysis and Multi-Modal Fusion of coronary Images |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The framework of this thesis is to study in detail different techniques and tools for medical image registration in order to ease the daily life of clinical experts in cardiology. The first aim of this thesis is providing computer tools for
fusing IVUS and angiogram data is of high clinical interest to help the physicians locate in IVUS data and decide which lesion is observed, how long it is, how far from a bifurcation or another lesions stays, etc. This thesis proves and
validates that we can segment the catheter path in angiographies using geodesic snakes (based on fast marching algorithm), a three-dimensional reconstruction of the catheter inspired in stereo vision and a new technique to fuse IVUS
and angiograms that establishes exact correspondences between them. We have developed a new workstation called iFusion that has four strong advantages: registration of IVUS and angiographic images with sub-pixel precision, it works on- and off-line, it is independent on the X-ray system and there is no need of daily calibration. The second aim of the thesis is devoted to developing a computer-aided analysis of IVUS for image-guided intervention. We have designed, implemented
and validated a robust algorithm for stent extraction and reconstruction from IVUS videos. We consider a very special and recent kind of stents, bioabsorbable stents that represent a great clinical challenge due to their property to be
absorbed by time and thus avoiding the “danger” of neostenosis as one of the main problems of metallic stents. We present a new and very promising algorithm based on an optimized cascade of multiple classifiers to automatically detect individual stent struts of a very novel bioabsorbable drug eluting coronary stent. This problem represents a very challenging target given the variability in contrast, shape and grey levels of the regions to be detected, what is
denoted by the high variability between the specialists (inter-observer variability of 0.14~$\pm$0.12). The obtained results of the automatic strut detection are within the inter-observer variability. |
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Barcelona (Espanya) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Petia Radeva |
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Admin @ si @ Rot2009 |
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1261 |
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