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Author Henry Velesaca; Patricia Suarez; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez; Angel Morera
Title (down) Video Analytics in Urban Environments: Challenges and Approaches Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities Abbreviated Journal
Volume 224 Issue Pages 101-121
Keywords
Abstract This chapter reviews state-of-the-art approaches generally present in the pipeline of video analytics on urban scenarios. A typical pipeline is used to cluster approaches in the literature, including image preprocessing, object detection, object classification, and object tracking modules. Then, a review of recent approaches for each module is given. Additionally, applications and datasets generally used for training and evaluating the performance of these approaches are included. This chapter does not pretend to be an exhaustive review of state-of-the-art video analytics in urban environments but rather an illustration of some of the different recent contributions. The chapter concludes by presenting current trends in video analytics in the urban scenario field.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ VSC2022 Serial 3811
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Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez
Title (down) Video Alignment for Difference-spotting Type Miscellaneous
Year 2008 Publication Proceedings of the ECCV workshop on Multi–camera and Multi–modal Sensor Fusion Algorithms and Applications (M2SFA2 2008), Marseille (France) Abbreviated Journal
Volume Issue Pages
Keywords video alignment
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ DPS2008 Serial 1079
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Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez
Title (down) Video Alignment for Change Detection Type Journal Article
Year 2011 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 20 Issue 7 Pages 1858-1869
Keywords video alignment
Abstract In this work, we address the problem of aligning two video sequences. Such alignment refers to synchronization, i.e., the establishment of temporal correspondence between frames of the first and second video, followed by spatial registration of all the temporally corresponding frames. Video synchronization and alignment have been attempted before, but most often in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, restrictive assumptions have been applied, including linear time correspondence or the knowledge of the complete trajectories of corresponding scene points; to some extent, these assumptions limit the practical applicability of any solutions developed. We intend to solve the more general problem of aligning video sequences recorded by independently moving cameras that follow similar trajectories, based only on the fusion of image intensity and GPS information. The novelty of our approach is to pose the synchronization as a MAP inference problem on a Bayesian network including the observations from these two sensor types, which have been proved complementary. Alignment results are presented in the context of videos recorded from vehicles driving along the same track at different times, for different road types. In addition, we explore two applications of the proposed video alignment method, both based on change detection between aligned videos. One is the detection of vehicles, which could be of use in ADAS. The other is online difference spotting videos of surveillance rounds.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; IF Approved no
Call Number DPS 2011; ADAS @ adas @ dps2011 Serial 1705
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Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez
Title (down) Video alignment for automotive applications Type Miscellaneous
Year 2009 Publication BMVA one–day technical meeting on vision for automotive applications Abbreviated Journal
Volume Issue Pages
Keywords video alignment
Abstract
Address London, UK
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ DPS2009 Serial 1271
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Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Petia Radeva
Title (down) VIBIKNet: Visual Bidirectional Kernelized Network for Visual Question Answering Type Conference Article
Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume Issue Pages
Keywords Visual Qestion Aswering; Convolutional Neural Networks; Long short-term memory networks
Abstract In this paper, we address the problem of visual question answering by proposing a novel model, called VIBIKNet. Our model is based on integrating Kernelized Convolutional Neural Networks and Long-Short Term Memory units to generate an answer given a question about an image. We prove that VIBIKNet is an optimal trade-off between accuracy and computational load, in terms of memory and time consumption. We validate our method on the VQA challenge dataset and compare it to the top performing methods in order to illustrate its performance and speed.
Address Faro; Portugal; June 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IbPRIA
Notes MILAB; no proj Approved no
Call Number Admin @ si @ BPC2017 Serial 2939
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Author Cristina Cañero; Petia Radeva
Title (down) Vesselness enhancement diffusion Type Journal Article
Year 2003 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 24 Issue 16 Pages 3141–3151
Keywords
Abstract IF: 0.809
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ CaR2003 Serial 371
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Author David Rotger; Petia Radeva; Oriol Rodriguez
Title (down) Vessel Tortuosity Extraction from IVUS Images Type Miscellaneous
Year 2006 Publication Computers in Cardiology (CiC´06), 33: 689–692 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Valencia (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RRR2006 Serial 762
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Author Aura Hernandez-Sabate; Petia Radeva; Antonio Tovar; Debora Gil
Title (down) Vessel structures alignment by spectral analysis of ivus sequences Type Conference Article
Year 2006 Publication Proc. of CVII, MICCAI Workshop Abbreviated Journal
Volume Issue Pages 39-36
Keywords
Abstract Three-dimensional intravascular ultrasound (IVUS) allows to visualize and obtain volumetric measurements of coronary lesions through an exploration of the cross sections and longitudinal views of arteries. However, the visualization and subsequent morpho-geometric measurements in IVUS longitudinal cuts are subject to distortion caused by periodic image/vessel motion around the IVUS catheter. Usually, to overcome the image motion artifact ECG-gating and image-gated approaches are proposed, leading to slowing the pullback acquisition or disregarding part of IVUS data. In this paper, we argue that the image motion is due to 3-D vessel geometry as well as cardiac dynamics, and propose a dynamic model based on the tracking of an elliptical vessel approximation to recover the rigid transformation and align IVUS images without loosing any IVUS data. We report an extensive validation with synthetic simulated data and in vivo IVUS sequences of 30 patients achieving an average reduction of the image artifact of 97% in synthetic data and 79% in real-data. Our study shows that IVUS alignment improves longitudinal analysis of the IVUS data and is a necessary step towards accurate reconstruction and volumetric measurements of 3-D IVUS.
Address
Corporate Author Thesis
Publisher Place of Publication Copenhaguen (Denmark), Editor
Language Summary Language Original Title
Series Editor Series Title 1st International Wokshop on Computer Vision for Intravascular and Intracardiac Imaging (CVII’06) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; MILAB Approved no
Call Number IAM @ iam @ HRT2006 Serial 1552
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Author Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados
Title (down) Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles Type Conference Article
Year 2010 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages 169-184
Keywords Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition
Abstract We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented.
Address Sousse, Tunisia
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CIFED
Notes DAG Approved no
Call Number DAG @ dag @ LBR2010a Serial 1293
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Author Daniel Ponsa; Antonio Lopez
Title (down) Vehicle Trajectory Estimation based on Monocular Vision Type Conference Article
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 Abbreviated Journal
Volume Issue Pages 587-594
Keywords vehicle detection
Abstract
Address Girona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ PoL2007a Serial 785
Permanent link to this record
 
 
Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez
Title (down) Vehicle geolocalization based on video synchronization Type Conference Article
Year 2010 Publication 13th Annual International Conference on Intelligent Transportation Systems Abbreviated Journal
Volume Issue Pages 1511–1516
Keywords video alignment
Abstract TC8.6
This paper proposes a novel method for estimating the geospatial localization of a vehicle. I uses as input a georeferenced video sequence recorded by a forward-facing camera attached to the windscreen. The core of the proposed method is an on-line video synchronization which finds out the corresponding frame in the georeferenced video sequence to the one recorded at each time by the camera on a second drive through the same track. Once found the corresponding frame in the georeferenced video sequence, we transfer its geospatial information of this frame. The key advantages of this method are: 1) the increase of the update rate and the geospatial accuracy with regard to a standard low-cost GPS and 2) the ability to localize a vehicle even when a GPS is not available or is not reliable enough, like in certain urban areas. Experimental results for an urban environments are presented, showing an average of relative accuracy of 1.5 meters.
Address Madeira Island (Portugal)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2153-0009 ISBN 978-1-4244-7657-2 Medium
Area Expedition Conference ITSC
Notes ADAS Approved no
Call Number ADAS @ adas @ DPS2010 Serial 1423
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title (down) Vegetation Index Estimation from Monospectral Images Type Conference Article
Year 2018 Publication 15th International Conference on Images Analysis and Recognition Abbreviated Journal
Volume 10882 Issue Pages 353-362
Keywords
Abstract This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
Address Povoa de Varzim; Portugal; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICIAR
Notes MSIAU; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ SSV2018c Serial 3196
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Author Philippe Dosch; Josep Llados
Title (down) Vectorial Signatures for Symbol Discrimination Type Miscellaneous
Year 2003 Publication Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ DoL2003 Serial 373
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Author Philippe Dosch; Josep Llados
Title (down) Vectorial Signatures for Symbol Discrimination Type Miscellaneous
Year 2004 Publication Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:150–161, ISBN: 3–540–22478–5 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Springer-Verlag
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ DoL2004 Serial 461
Permanent link to this record
 
 
Author Jaume Gibert
Title (down) Vector Space Embedding of Graphs via Statistics of Labelling Information Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.

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

In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Ernest Valveny
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ Gib2012 Serial 2204
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