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Author Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera
Title nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging Type Conference Article
Year 2022 Publication (up) International MICCAI Brainlesion Workshop Abbreviated Journal
Volume 12963 Issue Pages 540–551
Keywords Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN
Abstract In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1.
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 HUPBA; no menciona Approved no
Call Number Admin @ si @ JOM2022 Serial 3800
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Author Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez
Title Obstacle mapping module for quadrotors on outdoor Search and Rescue operations Type Conference Article
Year 2013 Publication (up) International Micro Air Vehicle Conference and Flight Competition Abbreviated Journal
Volume Issue Pages
Keywords UAV
Abstract 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.
Address Toulouse; France; September 2013
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 IMAV
Notes ADAS; 600.054; 600.057;IAM Approved no
Call Number Admin @ si @ NSH2013 Serial 2371
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Author Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes
Title On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition Type Conference Article
Year 2021 Publication (up) International Society for Music Information Retrieval Conference Abbreviated Journal
Volume Issue Pages 690-696
Keywords
Abstract Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts.
Address Virtual; November 2021
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 ISMIR
Notes DAG; 600.140; 600.121 Approved no
Call Number Admin @ si @ TBK2021 Serial 3616
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Author Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title Object Oriented Design of the DICOM standard Type Miscellaneous
Year 1996 Publication (up) International Symposium on Cardiovascular Imaging. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Leiden, The Netherlands
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 ISE @ ise @ VTV1996c Serial 104
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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 (up) 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
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Author A. M. Here; B. C. Lopez; Debora Gil; J. J. Camarero; Jordi Martinez-Vilalta
Title A new software to analyse wood anatomical features in conifer species Type Conference Article
Year 2013 Publication (up) International Symposium on Wood Structure in Plant Biology and Ecology Abbreviated Journal
Volume Issue Pages
Keywords
Abstract International Symposium on Wood Structure in Plant Biology and Ecology
Address Naples; Italy; March 2013
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 WSE
Notes IAM Approved no
Call Number Admin @ si @ HLG2013 Serial 2303
Permanent link to this record
 
 
Author Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu
Title Automatic Image-Based Waste Classification Type Conference Article
Year 2019 Publication (up) International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning Abbreviated Journal
Volume 11487 Issue Pages 422–431
Keywords Computer Vision; Deep learning; Convolutional neural networks; Waste classification
Abstract The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset.
Address Almeria; June 2019
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 IWINAC
Notes LAMP; 600.120 Approved no
Call Number RSV2019 Serial 3273
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Author Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva
Title Automatic Selection of Keyframes for Activity Recognition. Type Miscellaneous
Year 2000 Publication (up) International Workshop on Articulated Motion and Deformable Objects ( AMDO&rsquo), 173–181. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Palma de Mallorca.
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 ISE Approved no
Call Number ISE @ ise @ VGR2000b Serial 243
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Author Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas
Title Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets Type Conference Article
Year 2018 Publication (up) International Workshop on Artificial Intelligence and Pattern Recognition Abbreviated Journal
Volume 11047 Issue Pages 34-42
Keywords Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
Abstract The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
Address Cuba; September 2018
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 IWAIPR
Notes MILAB; no menciona Approved no
Call Number Admin @ si @ BGÑ2018 Serial 3237
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Author Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer
Title Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. Type Conference Article
Year 2020 Publication (up) International Workshop on Artificial Intelligence for Healthcare Applications Abbreviated Journal
Volume 12661 Issue Pages 476-489
Keywords
Abstract Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.
Address Virtual; January 2021
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 ICPRW
Notes DAG; 600.121; 600.140; Approved no
Call Number Admin @ si @ BCF2020 Serial 3508
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Author Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
Title Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study Type Conference Article
Year 2017 Publication (up) International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identication of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores.
Address Quebec; Canada; September 2017
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 MILAB; no proj Approved no
Call Number Admin @ si @ BCR2017 Serial 2968
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Author Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
Title Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images Type Conference Article
Year 2018 Publication (up) International Workshop on Egocentric Perception, Interaction and Computing at ECCV Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.
Address Munich; Alemanya; September 2018
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 ECCVW
Notes DAG; 600.129; 600.121; Approved no
Call Number Admin @ si @ BKB2018b Serial 3174
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Author Pau Riba; Alicia Fornes; Josep Llados
Title Towards the Alignment of Handwritten Music Scores Type Book Chapter
Year 2017 Publication (up) International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages 103-116
Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
Address
Corporate Author Thesis
Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-52158-9 Medium
Area Expedition Conference
Notes DAG; 600.097; 602.006; 600.121 Approved no
Call Number Admin @ si @ RFL2017 Serial 2955
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados
Title Ontology-Based Understanding of Architectural Drawings Type Book Chapter
Year 2017 Publication (up) International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages 75-85
Keywords Graphics recognition; Floor plan analysi; Domain ontology
Abstract In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.
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
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ HRL2017 Serial 3086
Permanent link to this record
 
 
Author Fadi Dornaika; Angel Sappa
Title 3D Motion from Image Derivatives using the Least Trimmed Square Regression Type Book Chapter
Year 2006 Publication (up) International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Xi'an (China)
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 @ DoS2006b Serial 690
Permanent link to this record