| |
Records |
Links |
|
Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
|
| |
Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
5th International Conference Games and Learning Alliance |
Abbreviated Journal |
|
|
| |
Volume |
10056 |
Issue |
|
Pages |
50-59 |
|
| |
Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
|
| |
Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
|
| |
Address |
|
|
| |
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 |
GALA |
|
| |
Notes |
ADAS;IAM; |
Approved |
no |
|
| |
Call Number |
HAC2016 |
Serial |
2864 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
|
| |
Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
14th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
9915 |
Issue |
|
Pages |
894-900 |
|
| |
Keywords |
Simulation environment; Automated Driving; Driver-Vehicle interaction |
|
| |
Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
|
| |
Address |
Amsterdam; The Netherlands; October 2016 |
|
| |
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 |
ECCVW |
|
| |
Notes |
ADAS;IAM; 600.085; 600.076 |
Approved |
no |
|
| |
Call Number |
MHE2016 |
Serial |
2865 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio |
|
| |
Title |
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition Workshops |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Semantic Segmentation |
|
| |
Abstract |
State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.
In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
|
| |
Address |
Honolulu; USA; July 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 |
CVPRW |
|
| |
Notes |
MILAB; ADAS; 600.076; 600.085; 601.281 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ JDV2016 |
Serial |
2866 |
|
| 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 |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure |
|
| |
Title |
Embedded Real-time Stixel Computation |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
GPU Technology Conference |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
GPU; CUDA; Stixels; Autonomous Driving |
|
| |
Abstract |
|
|
| |
Address |
Silicon Valley; USA; May 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 |
GTC |
|
| |
Notes |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ HEV2017a |
Serial |
2879 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
|
| |
Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
31st International Congress and Exhibition on Computer Assisted Radiology and Surgery |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Deep Learning; Medical Imaging |
|
| |
Abstract |
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation. |
|
| |
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 |
CARS |
|
| |
Notes |
ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ VBS2017a |
Serial |
2880 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
|
| |
Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
21st Ibero American Congress on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
467-475 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
|
| |
Address |
Lima; Perú; November 2016 |
|
| |
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 |
CIARP |
|
| |
Notes |
ADAS; 600.086 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CRS2016 |
Serial |
2913 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu |
|
| |
Title |
Joint coarse-and-fine reasoning for deep optical flow |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
24th International Conference on Image Processing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2558-2562 |
|
| |
Keywords |
|
|
| |
Abstract |
We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. |
|
| |
Address |
Beijing; China; September 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 |
ICIP |
|
| |
Notes |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ VRM2017 |
Serial |
2898 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez |
|
| |
Title |
Invertible conditional gans for image editing |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
30th Annual Conference on Neural Information Processing Systems Worshops |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications. |
|
| |
Address |
Barcelona; Spain; December 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 |
NIPSW |
|
| |
Notes |
LAMP; ADAS; 600.068 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PWR2016 |
Serial |
2906 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
|
| |
Title |
Cross-Spectral Image Patch Similarity using Convolutional Neural Network |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
|
| |
Address |
San Sebastian; Spain; May 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 |
ECMSM |
|
| |
Notes |
ADAS; 600.086; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SSV2017a |
Serial |
2916 |
|
| Permanent link to this record |
