| |
Records |
Links |
|
Author |
Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez |
|
| |
Title |
A reduced feature set for driver head pose estimation |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Applied Soft Computing |
Abbreviated Journal |
ASOC |
|
| |
Volume |
45 |
Issue  |
|
Pages |
98-107 |
|
| |
Keywords |
Head pose estimation; driving performance evaluation; subspace based methods; linear regression |
|
| |
Abstract |
Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. |
|
| |
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; 600.085; 600.076; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DHL2016 |
Serial |
2760 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
|
| |
Title |
Decremental generalized discriminative common vectors applied to images classification |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
| |
Volume |
131 |
Issue |
|
Pages |
46-57 |
|
| |
Keywords |
Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
|
| |
Abstract |
In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
|
| |
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; 600.118; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DMH2017a |
Serial |
3003 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina |
|
| |
Title |
CYBERH: Cyber-Physical Systems in Health for Personalized Assistance |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Assistance systems for e-Health applications have some specific requirements that demand of new methods for data gathering, analysis and modeling able to deal with SmallData:
1) systems should dynamically collect data from, both, the environment and the user to issue personalized recommendations; 2) data analysis should be able to tackle a limited number of samples prone to include non-informative data and possibly evolving in time due to changes in patient condition; 3) algorithms should run in real time with possibly limited computational resources and fluctuant internet access.
Electronic medical devices (and CyberPhysical devices in general) can enhance the process of data gathering and analysis in several ways: (i) acquiring simultaneously multiple sensors data instead of single magnitudes (ii) filtering data; (iii) providing real-time implementations condition by isolating tasks in individual processors of multiprocessors Systems-on-chip (MPSoC) platforms and (iv) combining information through sensor fusion
techniques.
Our approach focus on both aspects of the complementary role of CyberPhysical devices and analysis of SmallData in the process of personalized models building for e-Health applications. In particular, we will address the design of Cyber-Physical Systems in Health for Personalized Assistance (CyberHealth) in two specific application cases: 1) A Smart Assisted Driving System (SADs) for dynamical assessment of the driving capabilities of Mild Cognitive Impaired (MCI) people; 2) An Intelligent Operating Room (iOR) for improving the yield of bronchoscopic interventions for in-vivo lung cancer diagnosis. |
|
| |
Address |
Timisoara; Rumania; 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 |
SYNASC |
|
| |
Notes |
IAM; 600.085; 600.096; 600.075; 600.145 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GHC2017 |
Serial |
3045 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
|
| |
Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
| |
Volume |
145 |
Issue |
|
Pages |
219-235 |
|
| |
Keywords |
|
|
| |
Abstract |
With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
|
| |
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 |
0950-7051 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DFH2018 |
Serial |
3090 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |
|
| |
Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
| |
Volume |
6 |
Issue |
|
Pages |
18325 - 18334 |
|
| |
Keywords |
Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
|
| |
Abstract |
In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
|
| |
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 |
2169-3536 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DMH2018b |
Serial |
3091 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Santi Puch; Irina Sanchez; Aura Hernandez-Sabate; Gemma Piella; Vesna Prckovska |
|
| |
Title |
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
International MICCAI Brainlesion Workshop |
Abbreviated Journal |
|
|
| |
Volume |
11384 |
Issue |
|
Pages |
393-405 |
|
| |
Keywords |
Brain tumors; 3D fully-convolutional CNN; Magnetic resonance imaging; Global planar convolution |
|
| |
Abstract |
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge. |
|
| |
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 |
MICCAIW |
|
| |
Notes |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PSH2018 |
Serial |
3251 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua |
|
| |
Title |
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge |
Type |
Miscellaneous |
| |
Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST |
|
| |
Abstract |
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. |
|
| |
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; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BRJ2018 |
Serial |
3252 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil; Katerine Diaz; Carles Sanchez; Aura Hernandez-Sabate |
|
| |
Title |
Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images |
Type |
Miscellaneous |
| |
Year |
2020 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases. |
|
| |
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 |
IAM; 600.139; 600.145; 601.337 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GDS2020 |
Serial |
3474 |
|
| Permanent link to this record |
