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| Author | Xavier Baro | ||||
Title  |
Fast traffic sign detection on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #82 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | OR;HuPBA;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Bar2005 | Serial | 550 | ||
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| Author | Sergio Escalera | ||||
| Title |
Fast traffic model matching and recognition on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #84 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2005 | Serial | 572 | ||
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| Author | Fernando Lopez; J.M. Valiente; Ramon Baldrich; Maria Vanrell | ||||
| Title |
Fast surface grading using color statistics in the CIELab space | Type | Conference Article | ||
| Year | 2005 | Publication | Pattern Recognition and Image Analysis. IbPRIA 2005 | Abbreviated Journal | |
| Volume | LNCS 3523 | Issue | Pages | 66-673 | |
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| Address | Germany | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | LNCS | Series Title | Abbreviated Series Title | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ LVB2005 | Serial | 641 | ||
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| Author | Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados | ||||
| Title |
Fast Structural Matching for Document Image Retrieval through Spatial Databases | Type | Conference Article | ||
| Year | 2014 | Publication | Document Recognition and Retrieval XXI | Abbreviated Journal | |
| Volume | 9021 | Issue | Pages | ||
| Keywords | Document image retrieval; distance transform; MSER; spatial database | ||||
| Abstract | The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors. | ||||
| Address | Amsterdam; September 2014 | ||||
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| Area | Expedition | Conference | SPIE-DRR | ||
| Notes | DAG; 600.056; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ GRK2014a | Serial | 2496 | ||
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| Author | Jaume Amores; N. Sebe; Petia Radeva | ||||
| Title |
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors | Type | Miscellaneous | ||
| Year | 2005 | Publication | IEEE Computer Society, International Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(2):769–774 | Abbreviated Journal | |
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| Address | San Diego, CA (USA) | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | ADAS @ adas @ ASR2005a | Serial | 541 | ||
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| Author | Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva | ||||
| Title |
Fast Rigid Registration of Vascular Structures in IVUS Sequences | Type | Journal Article | ||
| Year | 2009 | Publication | IEEE Transactions on Information Technology in Biomedicine | Abbreviated Journal | |
| Volume | 13 | Issue | 6 | Pages | 106-1011 |
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| Abstract | Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1089-7771 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GPL2009 | Serial | 1250 | ||
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| Author | Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Marçal Rusiñol; Francesc J. Ferri | ||||
| Title |
Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction | Type | Journal Article | ||
| Year | 2018 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 60 | Issue | 4 | Pages | 512-524 |
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| Abstract | This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model. |
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| Notes | DAG; ADAS; 600.086; 600.130; 600.121; 600.118; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DMH2018a | Serial | 3062 | ||
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| Author | Sergio Escalera; Petia Radeva | ||||
| Title |
Fast greyscale road sign model matching and recognition | Type | Miscellaneous | ||
| Year | 2004 | Publication | CCIA, IOS Press | Abbreviated Journal | |
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| Address | Barcelona, Spain | ||||
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| Notes | HuPBA; MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EsR2004 | Serial | 469 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa | ||||
| Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 11 | Pages | 3249 |
| Keywords | stereo matching; deep learning; embedded GPU | ||||
| Abstract | Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. | ||||
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| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ AAN2020 | Serial | 3428 | ||
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| Author | Katerine Diaz; Francesc J. Ferri; W. Diaz | ||||
| Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates | Type | Conference Article | ||
| Year | 2013 | Publication | 20th International Conference On Neural Information Processing | Abbreviated Journal | |
| Volume | 8228 | Issue | III | Pages | 368-375 |
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| Abstract | An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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| Address | Daegu; Korea; November 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-42050-4 | Medium | |
| Area | Expedition | Conference | ICONIP | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ DFD2013 | Serial | 2439 | ||
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| Author | David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier | Type | Conference Article | ||
| Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1046–1053 | ||
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| Abstract | We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. | ||||
| Address | San Francisco; CA; USA; June 2010 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ARL2010a | Serial | 1311 | ||
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| Author | German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez | ||||
| Title |
Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios | Type | Conference Article | ||
| Year | 2013 | Publication | 24th British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | SLAM | ||||
| Abstract | Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC. Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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| Address | Bristol; UK; September 2013 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RGS2013b; ADAS @ adas @ | Serial | 2274 | ||
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| Author | Tomas Sixta; Julio C. S. Jacques Junior; Pau Buch Cardona; Eduard Vazquez; Sergio Escalera | ||||
| Title |
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12540 | Issue | Pages | 463-481 | |
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| Abstract | This work summarizes the 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge and provides a description of the top-winning solutions and analysis of the results. The aim of the challenge was to evaluate accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes. Participants were evaluated using an in-the-wild dataset based on reannotated IJB-C, further enriched 12.5K new images and additional labels. The dataset is not balanced, which simulates a real world scenario where AI-based models supposed to present fair outcomes are trained and evaluated on imbalanced data. The challenge attracted 151 participants, who made more 1.8K submissions in total. The final phase of the challenge attracted 36 active teams out of which 10 exceeded 0.999 AUC-ROC while achieving very low scores in the proposed bias metrics. Common strategies by the participants were face pre-processing, homogenization of data distributions, the use of bias aware loss functions and ensemble models. The analysis of top-10 teams shows higher false positive rates (and lower false negative rates) for females with dark skin tone as well as the potential of eyeglasses and young age to increase the false positive rates too. | ||||
| Address | Virtual; August 2020 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SJB2020 | Serial | 3499 | ||
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| Author | J. Filipe; Juan Andrade; J.L. Ferrier | ||||
| Title |
FAF 2005 | Type | Miscellaneous | ||
| Year | 2005 | Publication | Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, INSTICC Press | Abbreviated Journal | |
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| Address | Barcelona (Spain) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ FAF2005 | Serial | 609 | ||
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| Author | Marta Nuñez-Garcia; Sonja Simpraga; M.Angeles Jurado; Maite Garolera; Roser Pueyo; Laura Igual | ||||
| Title |
FADR: Functional-Anatomical Discriminative Regions for rest fMRI Characterization | Type | Conference Article | ||
| Year | 2015 | Publication | Machine Learning in Medical Imaging, Proceedings of 6th International Workshop, MLMI 2015, Held in Conjunction with MICCAI 2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 61-68 | ||
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| Address | Munich; Germany; October 2015 | ||||
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| Area | Expedition | Conference | MLMI | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ NSJ2015 | Serial | 2674 | ||
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