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Author |
Debora Gil; Agnes Borras; Manuel Ballester; Francesc Carreras; Ruth Aris; Manuel Vazquez; Enric Marti; Ferran Poveda |
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Title |
MIOCARDIA: Integrating cardiac function and muscular architecture for a better diagnosis |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Symposium on Applied Sciences in Biomedical and Communication Technologies |
Abbreviated Journal |
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Abstract |
Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. The MIOCARDIA project is a multidisciplinary project in cooperation with l'Hospital de la Santa Creu i de Sant Pau, Clinica la Creu Blanca and Barcelona Supercomputing Center. The ultimate goal of this project is defining a computational model of the myocardium. The model takes into account the deep interrelation between the anatomy and the mechanics of the heart. The paper explains the workflow of the MIOCARDIA project. It also introduces a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and provides evidences of a global helical organization. |
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Address |
Barcelona; Spain |
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Corporate Author |
Association for Computing Machinery |
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Place of Publication |
Barcelona, Spain |
Editor |
Association for Computing Machinery |
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Language |
english |
Summary Language |
english |
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ISBN |
978-1-4503-0913-4 |
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Conference |
ISABEL |
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Notes |
IAM |
Approved |
no |
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Call Number |
IAM @ iam @ GGB2011 |
Serial |
1691 |
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Permanent link to this record |
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Author |
Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru; Miguel Angel Gonzalez Ballester |
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Title |
Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Workshop on Computational and Clinical Applications in Abdominal Imaging |
Abbreviated Journal |
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Volume |
7029 |
Issue |
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Pages |
223–230 |
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Keywords |
medial manifolds, abdomen. |
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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. |
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Address |
Toronto; Canada; |
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Publisher |
Springer Link |
Place of Publication |
Berlin |
Editor |
H. Yoshida et al |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Title |
Lecture Notes in Computer Science |
Abbreviated Series Title |
LNCS |
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Series Volume |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-28556-1 |
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Expedition |
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Conference |
ABDI |
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Notes |
IAM;MV |
Approved |
no |
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Call Number |
IAM @ iam @ VGB2012 |
Serial |
1834 |
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Permanent link to this record |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
363-370 |
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Keywords |
Pedestrian Detection; Color; Part Based Models |
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Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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Language |
English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
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Series Editor |
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Series Title |
Lecture Notes on Computer Science |
Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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Expedition |
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Conference |
IbPRIA |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ RVL2011a |
Serial |
1666 |
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Permanent link to this record |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
6855 |
Issue |
II |
Pages |
463-470 |
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Keywords |
Pedestrian Detection; Color |
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Abstract |
Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. |
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Address |
Seville, Spain |
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Thesis |
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Publisher |
Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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Language |
English |
Summary Language |
english |
Original Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-23677-8 |
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Conference |
CAIP |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ RVL2011b |
Serial |
1665 |
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Permanent link to this record |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Cool world: domain adaptation of virtual and real worlds for human detection using active learning |
Type |
Conference Article |
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Year |
2011 |
Publication |
NIPS Domain Adaptation Workshop: Theory and Application |
Abbreviated Journal |
NIPS-DA |
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Volume |
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Issue |
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Pages |
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Keywords |
Pedestrian Detection; Virtual; Domain Adaptation; Active Learning |
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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. |
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Address |
Granada, Spain |
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Publisher |
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Place of Publication |
Granada, Spain |
Editor |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Conference |
DA-NIPS |
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Notes |
ADAS |
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no |
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Call Number |
ADAS @ adas @ VLP2011b |
Serial |
1756 |
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Permanent link to this record |
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Author |
David Geronimo; Frederic Lerasle; Antonio Lopez |
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Title |
State-driven particle filter for multi-person tracking |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th International Conference on Advanced Concepts for Intelligent Vision Systems |
Abbreviated Journal |
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Volume |
7517 |
Issue |
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Pages |
467-478 |
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Keywords |
human tracking |
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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. |
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Address |
Brno, Chzech Republic |
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Publisher |
Springer |
Place of Publication |
Heidelberg |
Editor |
J. Blanc-Talon et al. |
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Language |
English |
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Conference |
ACIVS |
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Notes |
ADAS |
Approved |
yes |
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Call Number |
GLL2012; ADAS @ adas @ gll2012a |
Serial |
1990 |
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Permanent link to this record |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
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Title |
Virtual Worlds and Active Learning for Human Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
393-400 |
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Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
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Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, 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, some authors 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 rendered images, i.e., using 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, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of 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 propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
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Address |
Alicante, Spain |
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Corporate Author |
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Thesis |
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Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
Editor |
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Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISBN |
978-1-4503-0641-6 |
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Conference |
ICMI |
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Notes |
ADAS |
Approved |
yes |
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Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
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Permanent link to this record |
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Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers |
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Title |
Adapting Pedestrian Detection from Synthetic to Far Infrared Images |
Type |
Conference Article |
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Year |
2013 |
Publication |
ICCV Workshop on Visual Domain Adaptation and Dataset Bias |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
Domain Adaptation; Far Infrared; Pedestrian Detection |
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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. |
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Address |
Sydney; Australia; December 2013 |
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Publisher |
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Place of Publication |
Sydney, Australy |
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Language |
English |
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Conference |
ICCVW-VisDA |
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Notes |
ADAS; 600.054; 600.055; 600.057; 601.217;ISE |
Approved |
no |
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Call Number |
ADAS @ adas @ SRV2013 |
Serial |
2334 |
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Permanent link to this record |