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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso
Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
Year 2017 Publication Journal of Thoracic Oncology Abbreviated Journal JTO
Volume 12 Issue 1S Pages (down) S596-S597
Keywords Thorax CT; diagnosis; Peripheral Pulmonary Nodule
Abstract A main weakness of virtual bronchoscopic navigation (VBN) is unsuccessful segmentation of distal branches approaching peripheral pulmonary nodules (PPN). CT scan acquisition protocol is pivotal for segmentation covering the utmost periphery. We hypothesize that application of continuous positive airway pressure (CPAP) during CT acquisition could improve visualization and segmentation of peripheral bronchi. The purpose of the present pilot study is to compare quality of segmentations under 4 CT acquisition modes: inspiration (INSP), expiration (EXP) and both with CPAP (INSP-CPAP and EXP-CPAP).
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; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ DGC2017a Serial 2883
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal
Title Product graph-based higher order contextual similarities for inexact subgraph matching Type Journal Article
Year 2018 Publication Pattern Recognition Abbreviated Journal PR
Volume 76 Issue Pages (down) 596-611
Keywords
Abstract Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.
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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 DAG; 602.167; 600.097; 600.121 Approved no
Call Number Admin @ si @ DLB2018 Serial 3083
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Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa
Title Multiple target tracking for intelligent headlights control Type Journal Article
Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 13 Issue 2 Pages (down) 594-605
Keywords Intelligent Headlights
Abstract Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.
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 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
Title Evaluating Color Representation for Online Road Detection Type Conference Article
Year 2013 Publication ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars Abbreviated Journal
Volume Issue Pages (down) 594-595
Keywords
Abstract Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired
using an on-board camera in different real-driving situations.
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 CVVT:E2M
Notes ADAS;ISE Approved no
Call Number Admin @ si @ AGL2013 Serial 2794
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Author Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva
Title Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder Type Journal Article
Year 2012 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG
Volume 36 Issue 8 Pages (down) 591-600
Keywords Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles
Abstract We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.
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 OR; HuPBA; MILAB Approved no
Call Number Admin @ si @ ISE2012 Serial 2143
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Author A.Kesidis; Dimosthenis Karatzas
Title Logo and Trademark Recognition Type Book Chapter
Year 2014 Publication Handbook of Document Image Processing and Recognition Abbreviated Journal
Volume D Issue Pages (down) 591-646
Keywords Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
Abstract The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor D. Doermann; K. Tombre
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-85729-858-4 Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ KeK2014 Serial 2425
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD Type Journal
Year 2022 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal
Volume 13 Issue Pages (down) 591–611
Keywords
Abstract One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKE2022a Serial 3660
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Author Oriol Ramos Terrades; Ernest Valveny
Title A new use of the ridgelets transform for describing linear singularities in images Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue 6 Pages (down) 587–596
Keywords
Abstract
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 DAG Approved no
Call Number DAG @ dag @ RaV2006a Serial 635
Permanent link to this record
 
 
Author Daniel Ponsa; Antonio Lopez
Title Vehicle Trajectory Estimation based on Monocular Vision Type Conference Article
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 Abbreviated Journal
Volume Issue Pages (down) 587-594
Keywords vehicle detection
Abstract
Address Girona (Spain)
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 Approved no
Call Number ADAS @ adas @ PoL2007a Serial 785
Permanent link to this record
 
 
Author David Masip; Jordi Vitria
Title Shared Feature Extraction for Nearest Neighbor Face Recognition Type Journal
Year 2008 Publication IEEE Transactions on Neural Networks Abbreviated Journal
Volume 19 Issue 4 Pages (down) 586–595
Keywords
Abstract
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 OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ MaV2008 Serial 944
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez
Title Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features Type Conference Article
Year 2012 Publication 12th European Conference on Computer Vision – Workshops and Demonstrations Abbreviated Journal
Volume 7584 Issue Pages (down) 586-595
Keywords road detection
Abstract Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo.
Address
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 978-3-642-33867-0 Medium
Area Expedition Conference ECCVW
Notes ADAS;ISE Approved no
Call Number Admin @ si @ ALG2012; ADAS @ adas Serial 2187
Permanent link to this record
 
 
Author David Masip; Agata Lapedriza; Jordi Vitria
Title Multitask Learning: An Application to Incremental Face Recognition Type Conference Article
Year 2008 Publication 3rd International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume 1 Issue Pages (down) 585–590
Keywords
Abstract
Address Madeira (Portugal)
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 VISAPP
Notes OR; MV Approved no
Call Number BCNPCL @ bcnpcl @ MLV2008 Serial 979
Permanent link to this record
 
 
Author David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez
Title 2D-3D based on-board pedestrian detection system Type Journal Article
Year 2010 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 114 Issue 5 Pages (down) 583–595
Keywords Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms
Abstract During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system.
Address Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595
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 1077-3142 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ GSP2010 Serial 1341
Permanent link to this record
 
 
Author Gabriel Villalonga; Joost Van de Weijer; Antonio Lopez
Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
Year 2020 Publication Sensors Abbreviated Journal SENS
Volume 20 Issue 3 Pages (down) 583
Keywords
Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.
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 LAMP; ADAS; 600.118; 600.120 Approved no
Call Number Admin @ si @ VWL2020 Serial 3405
Permanent link to this record
 
 
Author Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title A Generic Image Retrieval Method for Date Estimation of Historical Document Collections Type Conference Article
Year 2022 Publication Document Analysis Systems.15th IAPR International Workshop, (DAS2022) Abbreviated Journal
Volume 13237 Issue Pages (down) 583–597
Keywords Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG
Abstract Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images.
Address La Rochelle, France; May 22–25, 2022
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 DAS
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ MGR2022 Serial 3694
Permanent link to this record