|
Records |
Links |
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Joan Serrat; Ferran Diego; Jose Manuel Alvarez; Felipe Lumbreras |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Alignment of Videos Recorded from Moving Vehicles |
Type |
Conference Article |
|
Year |
2007 |
Publication |
in 14th International Conference on Image Analysis and Processing, |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
512–517 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Modena (Italia) |
|
|
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 @ SDA2007 |
Serial |
879 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Synchronization of Video Sequences from Free-moving Cameras |
Type |
Conference Article |
|
Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
|
|
Volume |
4477 |
Issue |
|
Pages |
620–627 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Girona (Spain) |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
J. Marti et al. |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
IbPRIA |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ SDL2007 |
Serial |
880 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Joan Serrat; Enric Marti |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Elastic matching using interpolation splines |
Type |
Conference Article |
|
Year |
1991 |
Publication |
IV Spanish Symposium of Pattern Recognition and image Analysis |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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;IAM; |
Approved |
no |
|
|
Call Number |
IAM @ iam @ SMV1991 |
Serial |
1651 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Joan Serrat; Antonio Lopez; David Lloret |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
On ridges and valleys. |
Type |
Conference Article |
|
Year |
2000 |
Publication |
15 th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
4 |
Issue |
|
Pages |
59-66 |
|
|
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 |
ICPR |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ SLL2000 d |
Serial |
334 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Joan Serrat |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Aplicacion del analisis de imagenes en radiologia. |
Type |
Conference Article |
|
Year |
1995 |
Publication |
VI National Simposium on Pattern Recognition and image Analysis |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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 |
IbPRIA |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ Ser1995 |
Serial |
120 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
Type |
Conference Article |
|
Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
3886 - 3891 |
|
|
Keywords |
Domain Adaptation; Pedestrian Detection |
|
|
Abstract |
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
|
|
Address |
Stockholm; Sweden; August 2014 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1051-4651 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICPR |
|
|
Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ XRV2014a |
Serial |
2434 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
|
|
Title |
Multi-task Bilinear Classifiers for Visual Domain Adaptation |
Type |
Conference Article |
|
Year |
2013 |
Publication |
Advances in Neural Information Processing Systems Workshop |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Domain Adaptation; Pedestrian Detection; ADAS |
|
|
Abstract |
We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
|
|
Address |
Lake Tahoe; Nevada; USA; December 2013 |
|
|
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 |
NIPSW |
|
|
Notes |
ADAS; 600.054; 600.057; 601.217;ISE |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ XRH2013 |
Serial |
2340 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Incremental Domain Adaptation of Deformable Part-based Models |
Type |
Conference Article |
|
Year |
2014 |
Publication |
25th British Machine Vision Conference |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Pedestrian Detection; Part-based models; Domain Adaptation |
|
|
Abstract |
Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
|
|
Address |
Nottingham; uk; September 2014 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
BMVA Press |
Place of Publication |
|
Editor |
Valstar, Michel and French, Andrew and Pridmore, Tony |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
BMVC |
|
|
Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
|
|
Call Number |
XRV2014c; ADAS @ adas @ xrv2014c |
Serial |
2455 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
DA-DPM Pedestrian Detection |
Type |
Conference Article |
|
Year |
2013 |
Publication |
ICCV Workshop on Reconstruction meets Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Domain Adaptation; Pedestrian Detection |
|
|
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 |
ICCVW-RR |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ XRV2013 |
Serial |
2569 |
|
Permanent link to this record |
|
|
|
|
Author ![sorted by Author field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
Type |
Conference Article |
|
Year |
2019 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2379-2384 |
|
|
Keywords |
|
|
|
Abstract |
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
|
|
Address |
Montreal; Canada; May 2019 |
|
|
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 |
ICRA |
|
|
Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ XWY2018 |
Serial |
3182 |
|
Permanent link to this record |