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Author Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers
Title Adapting Pedestrian Detection from Synthetic to Far Infrared Images Type Conference Article
Year 2013 Publication ICCV Workshop on Visual Domain Adaptation and Dataset Bias Abbreviated Journal
Volume Issue Pages
Keywords Domain Adaptation; Far Infrared; Pedestrian Detection
Abstract We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes.
Address Sydney; Australia; December 2013
Corporate Author Thesis
Publisher Place of Publication Sydney, Australy Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCVW-VisDA
Notes ADAS; 600.054; 600.055; 600.057; 601.217;ISE Approved no
Call Number ADAS @ adas @ SRV2013 Serial (down) 2334
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Author David Vazquez
Title Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection Type Book Whole
Year 2013 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal
Volume 1 Issue 1 Pages 1-105
Keywords Pedestrian Detection; Domain Adaptation
Abstract Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area.
Address Barcelona
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Barcelona Editor Antonio Lopez;Daniel Ponsa
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940530-1-6 Medium
Area Expedition Conference
Notes adas Approved yes
Call Number ADAS @ adas @ Vaz2013 Serial (down) 2276
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Author Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
Title Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers Type Conference Article
Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal
Volume Issue Pages 688 - 693
Keywords Pedestrian Detection; Domain Adaptation
Abstract Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.
Address Portland; oregon; June 2013
Corporate Author Thesis
Publisher Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.054; 600.057; 601.217 Approved yes
Call Number XVR2013; ADAS @ adas @ xvr2013a Serial (down) 2220
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Author David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
Title Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes Type Conference Article
Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal
Volume Issue Pages 706 - 711
Keywords Pedestrian Detection; Domain Adaptation
Abstract Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
Address Portland; Oregon; June 2013
Corporate Author Thesis
Publisher IEEE Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.054; 600.057; 601.217 Approved no
Call Number ADAS @ adas @ VXR2013a Serial (down) 2219
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Author David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo
Title Interactive Training of Human Detectors Type Book Chapter
Year 2013 Publication Multiodal Interaction in Image and Video Applications Abbreviated Journal
Volume 48 Issue Pages 169-182
Keywords Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation
Abstract Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations.
Address Springer Heidelberg New York Dordrecht London
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1868-4394 ISBN 978-3-642-35931-6 Medium
Area Expedition Conference
Notes ADAS; 600.057; 600.054; 605.203 Approved no
Call Number VLP2013; ADAS @ adas @ vlp2013 Serial (down) 2193
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Author David Geronimo; Frederic Lerasle; Antonio Lopez
Title State-driven particle filter for multi-person tracking Type Conference Article
Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal
Volume 7517 Issue Pages 467-478
Keywords human tracking
Abstract Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.
Address Brno, Chzech Republic
Corporate Author Thesis
Publisher Springer Place of Publication Heidelberg Editor J. Blanc-Talon et al.
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ACIVS
Notes ADAS Approved yes
Call Number GLL2012; ADAS @ adas @ gll2012a Serial (down) 1990
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Author Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers
Title Improving HOG with Image Segmentation: Application to Human Detection Type Conference Article
Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal
Volume 7517 Issue Pages 178-189
Keywords Segmentation; Pedestrian Detection
Abstract In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Address Brno, Czech Republic
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor J. Blanc-Talon et al.
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33139-8 Medium
Area Expedition Conference ACIVS
Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ SLV2012 Serial (down) 1980
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Author Javier Marin; David Geronimo; David Vazquez; Antonio Lopez
Title Pedestrian Detection: Exploring Virtual Worlds Type Book Chapter
Year 2012 Publication Handbook of Pattern Recognition: Methods and Application Abbreviated Journal
Volume 5 Issue Pages 145-162
Keywords Virtual worlds; Pedestrian Detection; Domain Adaptation
Abstract Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition.
Address
Corporate Author Thesis
Publisher iConcept Press Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-477554-82-1 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ MGV2012 Serial (down) 1979
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Author Patricia Marquez; Debora Gil ; Aura Hernandez-Sabate
Title Error Analysis for Lucas-Kanade Based Schemes Type Conference Article
Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 184-191
Keywords Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
Abstract Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Address Aveiro, Portugal
Corporate Author Thesis
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Editor
Language english Summary Language Original Title
Series Editor Campilho, Aurélio and Kamel, Mohamed Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-31294-6 Medium
Area Expedition Conference ICIAR
Notes IAM Approved no
Call Number IAM @ iam @ MGH2012a Serial (down) 1899
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Author Albert Andaluz
Title Harmonic Phase Flow: User's guide Type Manual
Year 2012 Publication CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract HPF is a plugin for the computation of clinical scores under Osirix.
This manual provides a basic guide for experienced clinical staff. Chapter 1 provides the theoretical background in which this plugin is based.
Next, in chapter 2 we provide basic instructions for installing and uninstalling this plugin. chapter 3we shows a step-by-step scenario to compute clinical scores from tagged-MRI images with HPF. Finally, in chapter 4 we provide a quick guide for plugin developers
Address Bellaterra, Barcelona (Spain)
Corporate Author Computer Vision Center Thesis
Publisher CVC Place of Publication Barcelona Editor
Language english Summary Language english Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM Approved no
Call Number IAM @ iam @ And2012 Serial (down) 1863
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Author David Roche; Debora Gil; Jesus Giraldo
Title Assessing agonist efficacy in an uncertain Em world Type Conference Article
Year 2012 Publication 40th Keystone Symposia on mollecular and celular biology Abbreviated Journal
Volume Issue Pages 79
Keywords
Abstract The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed.
Address Fairmont Banff Springs, Banff, Alberta, Canada
Corporate Author Keystone Symposia Thesis
Publisher Keystone Symposia Place of Publication Editor A. Christopoulus and M. Bouvier
Language english Summary Language english Original Title
Series Editor Keystone Symposia Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference KSMCB
Notes IAM Approved no
Call Number IAM @ iam @ RGG2012 Serial (down) 1855
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Author Carles Sanchez
Title Tracheal ring detection in bronchoscopy Type Report
Year 2011 Publication CVC Technical Report Abbreviated Journal
Volume 168 Issue Pages
Keywords Bronchoscopy, tracheal ring, segmentation
Abstract Endoscopy is the process in which a camera is introduced inside a human.
Given that endoscopy provides realistic images (in contrast to other modalities) and allows non-invase minimal intervention procedures (which can aid in diagnosis and surgical interventions), its use has spreaded during last decades.
In this project we will focus on bronchoscopic procedures, during which the camera is introduced through the trachea in order to have a diagnostic of the patient. The diagnostic interventions are focused on: degree of stenosis (reduction in tracheal area), prosthesis or early diagnosis of tumors. In the first case, assessment of the luminal area and the calculation of the diameters of the tracheal rings are required. A main limitation is that all the process is done by hand,
which means that the doctor takes all the measurements and decisions just by looking at the screen. As far as we know there is no computational framework for helping the doctors in the diagnosis.
This project will consist of analysing bronchoscopic videos in order to extract useful information for the diagnostic of the degree of stenosis. In particular we will focus on segmentation of the tracheal rings. As a result of this project several strategies (for detecting tracheal rings) had been implemented in order to compare their performance.
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor Debora Gil, F.Javier Sanchez
Language english Summary Language english Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MV Approved no
Call Number IAM @ iam @ San2011 Serial (down) 1841
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Author Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester
Title Multilocal Creaseness Measure Type Journal
Year 2012 Publication The Insight Journal Abbreviated Journal IJ
Volume Issue Pages
Keywords Ridges, Valley, Creaseness, Structure Tensor, Skeleton,
Abstract This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.
Address
Corporate Author Alma IT Systems Thesis
Publisher Place of Publication Editor
Language english Summary Language english Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;ADAS; Approved no
Call Number IAM @ iam @ VGL2012 Serial (down) 1840
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Author Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru; Miguel Angel Gonzalez Ballester
Title Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs Type Book Chapter
Year 2012 Publication Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume 7029 Issue Pages 223–230
Keywords medial manifolds, abdomen.
Abstract Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations.
Address Toronto; Canada;
Corporate Author Thesis
Publisher Springer Link Place of Publication Berlin Editor H. Yoshida et al
Language English Summary Language English Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-28556-1 Medium
Area Expedition Conference ABDI
Notes IAM;MV Approved no
Call Number IAM @ iam @ VGB2012 Serial (down) 1834
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Author David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin
Title Cool world: domain adaptation of virtual and real worlds for human detection using active learning Type Conference Article
Year 2011 Publication NIPS Domain Adaptation Workshop: Theory and Application Abbreviated Journal NIPS-DA
Volume Issue Pages
Keywords Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
Abstract Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
Address Granada, Spain
Corporate Author Thesis
Publisher Place of Publication Granada, Spain Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DA-NIPS
Notes ADAS Approved no
Call Number ADAS @ adas @ VLP2011b Serial (down) 1756
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