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Author Enrique Cabello; Cristina Conde; Angel Serrano; Licesio Rodriguez; David Vazquez edit   pdf
openurl 
  Title Empleo de sistemas biométricos para el reconocimiento de personas en aeropuertos Type Journal Article
  Year 2006 Publication Instituto Universitario de Investigación sobre Seguridad Interior (IUSI 2006) Abbreviated Journal  
  Volume (down) Issue Pages  
  Keywords Surveillance; Face detection; Face recognition  
  Abstract El presente proyecto se desarrolló a lo largo del año 2005, probando un prototipo de un sistema de verificación facial con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. Se diseñaron varios experimentos, agrupados en dos clases. En el primer tipo, el sistema es entrenado con imágenes obtenidas en condiciones de laboratorio y luego probado con imágenes extraídas de las cámaras de video vigilancia del aeropuerto de Barajas. En el segundo caso, tanto las imágenes de entrenamiento como las de prueba corresponden a imágenes extraídas de Barajas. Se ha desarrollado un sistema completo, que incluye adquisición y digitalización de las imágenes, localización y recorte de las caras en escena, verificación de sujetos y obtención de resultados. Los resultados muestran, que, en general, un sistema de verificación facial basado en imágenes puede ser una ayuda a un operario que deba estar vigilando amplias zonas.  
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  Notes invisible;ADAS Approved no  
  Call Number ADAS @ adas @ CCS2006a Serial 1672  
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Author Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester edit   pdf
url  openurl
  Title Multilocal Creaseness Measure Type Journal
  Year 2012 Publication The Insight Journal Abbreviated Journal IJ  
  Volume (down) Issue Pages  
  Keywords Ridges, Valley, Creaseness, Structure Tensor, Skeleton,  
  Abstract This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission.  
  Address  
  Corporate Author Alma IT Systems Thesis  
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  Language english Summary Language english Original Title  
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  Area Expedition Conference  
  Notes IAM;ADAS; Approved no  
  Call Number IAM @ iam @ VGL2012 Serial 1840  
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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez-Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville edit   pdf
url  openurl
  Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Journal Article
  Year 2017 Publication Journal of Healthcare Engineering Abbreviated Journal JHCE  
  Volume (down) Issue Pages 2040-2295  
  Keywords Colonoscopy images; Deep Learning; Semantic Segmentation  
  Abstract Colorectal cancer (CRC) is the third cause of cancer death world-wide. 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) aim- ing 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 segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.  
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  Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 Approved no  
  Call Number VBS2017b Serial 2940  
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Author Gabriel Villalonga; Antonio Lopez edit   pdf
doi  openurl
  Title Co-Training for On-Board Deep Object Detection Type Journal Article
  Year 2020 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume (down) Issue Pages 194441 - 194456  
  Keywords  
  Abstract Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ ViL2020 Serial 3488  
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Author Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Multimodal end-to-end autonomous driving Type Journal Article
  Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume (down) Issue Pages 1-11  
  Keywords  
  Abstract A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.  
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  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ XCG2020 Serial 3490  
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