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Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Obstacle mapping module for quadrotors on outdoor Search and Rescue operations |
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Conference Article |
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2013 |
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International Micro Air Vehicle Conference and Flight Competition |
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UAV |
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Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments. |
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Toulouse; France; September 2013 |
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ADAS; 600.054; 600.057;IAM |
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Admin @ si @ NSH2013 |
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2371 |
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Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis |
![find record details (via OpenURL) openurl](img/xref.gif)
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Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification |
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2017 |
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3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition |
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Thessaloniki; Greece; October 2017 |
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ADAS; 600.118 |
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Admin @ si @ GDM2017 |
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3081 |
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Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Va-ID: A trust-based virtual assessment system |
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Conference Article |
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2014 |
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6th International Conference on Intelligent Networking and Collaborative Systems |
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328 - 335 |
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Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model. |
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Salerna; Italy; September 2014 |
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978-1-4799-6386-7 |
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OR; HuPBA;MV |
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Admin @ si @ ABB2014 |
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2620 |
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Joan Arnedo-Moreno; Agata Lapedriza |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Visualizing key authenticity: turning your face into your public key |
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Conference Article |
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2010 |
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6th China International Conference on Information Security and Cryptology |
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605-618 |
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Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process. |
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Inscrypt |
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OR;MV |
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Admin @ si @ ArL2010c |
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2149 |
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Author |
Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy |
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Conference Article |
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2015 |
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6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 |
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10 |
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6 |
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935-945 |
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PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done. |
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Barcelona; Spain; June 2015 |
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IAM; MV; 600.075 |
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Admin @ si @ SBS2015b |
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2613 |
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Author |
Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera |
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Title |
Análisis Comportamental Automatizado de TDAH: la Influencia de la Variable Motivación |
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Conference Article |
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2012 |
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IPSI – Cosmocaixa, Jornadas "Empremtes del present, efectes en la psicoanàlisi, la cultura i la societat |
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MILAB; HuPBA; OR |
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Admin @ si @ EMI2012b |
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2065 |
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Kamal Nasrollahi; Sergio Escalera; P. Rasti; Gholamreza Anbarjafari; Xavier Baro; Hugo Jair Escalante; Thomas B. Moeslund |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Deep Learning based Super-Resolution for Improved Action Recognition |
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Conference Article |
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2015 |
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5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 |
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67 - 72 |
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Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos. |
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Orleans; France; November 2015 |
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HuPBA;MV |
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no |
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Admin @ si @ NER2015 |
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2648 |
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Author |
Xavier Soria; Angel Sappa; Arash Akbarinia |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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Conference Article |
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2017 |
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7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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NEUROBIT; MSIAU; 600.122 |
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Admin @ si @ SSA2017 |
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3074 |
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Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Temporal Coherence Analysis for Intelligent Headlight Control |
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Miscellaneous |
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2008 |
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2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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59–64 |
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Intelligent Headlights |
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ADAS;CIC |
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ADAS @ adas @ LHB2008b |
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1112 |
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R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
![download PDF file pdf](img/file_PDF.gif)
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On-line Semantic Perception Using Uncertainty |
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Conference Article |
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2012 |
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International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Semantic Segmentation |
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Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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ADAS |
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ADAS @ adas @ NRR2012 |
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2378 |
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Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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Conference Article |
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2015 |
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International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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ADAS; 600.076 |
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Admin @ si @ OSL2015 |
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2664 |
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Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
![goto web page url](img/www.gif)
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Title |
Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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Conference Article |
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2023 |
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International Conference on Intelligent Robots and Systems |
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On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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Admin @ si @ XCP2023 |
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3930 |
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Author |
Maria Oliver; Gloria Haro; Mariella Dimiccoli; Baptiste Mazin; Coloma Ballester |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A computational model of amodal completion |
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Conference Article |
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2016 |
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SIAM Conference on Imaging Science |
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This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling. |
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Albuquerque; New Mexico; USA; May 2016 |
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MILAB; 601.235 |
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Admin @ si @OHD2016a |
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2788 |
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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 |
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14th International Symposium on Applied Sciences in Biomedical and Communication Technologies |
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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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Barcelona; Spain |
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Association for Computing Machinery |
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Barcelona, Spain |
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Association for Computing Machinery |
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english |
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english |
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978-1-4503-0913-4 |
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IAM |
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IAM @ iam @ GGB2011 |
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1691 |
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Author |
Simone Balocco; Carlo Gatta; Xavier Carrillo; F. Mauri; Petia Radeva |
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Plaque Type, Plaque Burden and Wall Shear Stress Relation in Coronary Arteries Assessed by X-ray Angiography and Intravascular Ultrasound: a Qualitative Study |
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2011 |
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14th International Symposium on Applied Sciences in Biomedical and Communication Technologies |
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In this paper, we present a complete framework that automatically provides fluid-dynamic and plaque analysis from IVUS and Angiographic sequences. Such framework is used to analyze, in three coronary arteries, the relation between wall shear stress with type and amount of plaque. Preliminary qualitative results show an inverse relation between the wall shear stress and the plaque burden, which is confirmed by the fact that the plaque growth is higher on the wall having concave curvature. Regarding the plaque type it was observed that regions having low shear stress are predominantly fibro-lipidic while the heavy calcifications are in general located in areas of the vessel having high WSS. |
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978-1-4503-0913-4 |
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MILAB |
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Admin @ si @ BGC2011b |
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1799 |
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