| |
Records |
Links |
|
Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
|
| |
Title |
Single view facial hair 3D reconstruction |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
|
| |
Volume |
11867 |
Issue |
|
Pages |
423-436 |
|
| |
Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling |
|
| |
Abstract |
n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
|
| |
Address  |
Madrid; July 2019 |
|
| |
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 |
IbPRIA |
|
| |
Notes |
ADAS; 600.086; 600.130; 600.122 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
3707 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras |
|
| |
Title |
Information Theoretic Rotationwise Robust Binary Descriptor Learning |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
368-378 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. |
|
| |
Address |
Mérida; Mexico; November 2016 |
|
| |
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 |
S+SSPR |
|
| |
Notes |
DAG; ADAS; 600.097; 600.086 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RLL2016 |
Serial |
2871 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
|
| |
Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
Type |
Conference Article |
| |
Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
565–572 |
|
| |
Keywords |
road detection |
|
| |
Abstract |
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
|
| |
Address |
Miami (USA) |
|
| |
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 |
1063-6919 |
ISBN |
978-1-4244-3992-8 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ AGL2009 |
Serial |
1169 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joan Serrat; Ferran Diego; Jose Manuel Alvarez; Felipe Lumbreras |
|
| |
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 |
Fadi Dornaika; Angel Sappa |
|
| |
Title |
Real-time Vehicle Ego-Motion using Stereo Pairs and Particle Filters |
Type |
Conference Article |
| |
Year |
2007 |
Publication |
Int. Conf. on Image Analysis and Recognition, |
Abbreviated Journal |
|
|
| |
Volume |
4633 |
Issue |
|
Pages |
469–480 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Montreal (Canada) |
|
| |
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 |
|
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ DoS2007a |
Serial |
813 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
|
| |
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 |
| |
|
| |
|
Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
|
| |
Title |
CARLA: An Open Urban Driving Simulator |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
Abbreviated Journal |
|
|
| |
Volume |
78 |
Issue |
|
Pages |
1-16 |
|
| |
Keywords |
Autonomous driving; sensorimotor control; simulation |
|
| |
Abstract |
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
|
| |
Address |
Mountain View; CA; USA; November 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 |
CORL |
|
| |
Notes |
ADAS; 600.085; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DRC2017 |
Serial |
2988 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
|
| |
Title |
On Offline Evaluation of Vision-based Driving Models |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
11219 |
Issue |
|
Pages |
246-262 |
|
| |
Keywords |
Autonomous driving; deep learning |
|
| |
Abstract |
Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and
suitable offline metrics. |
|
| |
Address |
Munich; September 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 |
ECCV |
|
| |
Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CLK2018 |
Serial |
3162 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
|
| |
Title |
Towards Query-by-Speech Handwritten Keyword Spotting |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
501-505 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
|
| |
Address |
Nancy; France; August 2015 |
|
| |
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 |
ICDAR |
|
| |
Notes |
DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RAT2015b |
Serial |
2682 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fernando Barrera; Felipe Lumbreras; Cristhian Aguilera; Angel Sappa |
|
| |
Title |
Planar-Based Multispectral Stereo |
Type |
Conference Article |
| |
Year |
2012 |
Publication |
11th Quantitative InfraRed Thermography |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Naples, Italy |
|
| |
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 |
QIRT |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BLA2012 |
Serial |
2016 |
|
| Permanent link to this record |
