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Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera |
![goto web page url](img/www.gif)
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Title |
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging |
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Conference Article |
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Year |
2022 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International MICCAI Brainlesion Workshop |
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12963 |
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540–551 |
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Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN |
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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. |
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MICCAIW |
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HUPBA; no menciona |
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no |
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Admin @ si @ JOM2022 |
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3800 |
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Author |
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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IMAV |
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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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Author |
Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
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Conference Article |
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Year |
2021 |
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International Society for Music Information Retrieval Conference |
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690-696 |
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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. |
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Virtual; November 2021 |
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ISMIR |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ TBK2021 |
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3616 |
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Author |
Craig Von Land; Ricardo Toledo; Juan J. Villanueva |
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Title |
Object Oriented Design of the DICOM standard |
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Miscellaneous |
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1996 |
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International Symposium on Cardiovascular Imaging. |
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Leiden, The Netherlands |
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ADAS |
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ISE @ ise @ VTV1996c |
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104 |
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Author |
Debora Gil; Aura Hernandez-Sabate; David Castells; Jordi Carrabina |
![download PDF file pdf](img/file_PDF.gif)
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Title |
CYBERH: Cyber-Physical Systems in Health for Personalized Assistance |
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Conference Article |
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2017 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Symposium on Symbolic and Numeric Algorithms for Scientific Computing |
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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. |
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Timisoara; Rumania; September 2017 |
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SYNASC |
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IAM; 600.085; 600.096; 600.075; 600.145 |
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no |
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Admin @ si @ GHC2017 |
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3045 |
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Author |
A. M. Here; B. C. Lopez; Debora Gil; J. J. Camarero; Jordi Martinez-Vilalta |
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Title |
A new software to analyse wood anatomical features in conifer species |
Type |
Conference Article |
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2013 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Symposium on Wood Structure in Plant Biology and Ecology |
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International Symposium on Wood Structure in Plant Biology and Ecology |
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Naples; Italy; March 2013 |
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WSE |
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IAM |
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no |
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Admin @ si @ HLG2013 |
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2303 |
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Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Image-Based Waste Classification |
Type |
Conference Article |
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Year |
2019 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning |
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11487 |
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Pages |
422–431 |
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Computer Vision; Deep learning; Convolutional neural networks; Waste classification |
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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. |
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Almeria; June 2019 |
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IWINAC |
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LAMP; 600.120 |
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RSV2019 |
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3273 |
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Author |
Javier Varona; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva |
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Title |
Automatic Selection of Keyframes for Activity Recognition. |
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Miscellaneous |
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2000 |
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International Workshop on Articulated Motion and Deformable Objects ( AMDO&rsquo), 173–181. |
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Palma de Mallorca. |
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ISE |
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ISE @ ise @ VGR2000b |
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243 |
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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
![goto web page url](img/www.gif)
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
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2018 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Workshop on Artificial Intelligence and Pattern Recognition |
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11047 |
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34-42 |
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Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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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. |
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Cuba; September 2018 |
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IWAIPR |
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MILAB; no menciona |
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no |
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Admin @ si @ BGÑ2018 |
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3237 |
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Author |
Asma Bensalah; Jialuo Chen; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados; Miguel A. Ferrer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches. |
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Conference Article |
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2020 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Workshop on Artificial Intelligence for Healthcare Applications |
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12661 |
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476-489 |
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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. |
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Virtual; January 2021 |
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ICPRW |
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DAG; 600.121; 600.140; |
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Admin @ si @ BCF2020 |
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3508 |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study |
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Conference Article |
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2017 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) |
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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. |
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Quebec; Canada; September 2017 |
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MICCAIW |
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MILAB; no proj |
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no |
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Admin @ si @ BCR2017 |
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2968 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
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Conference Article |
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2018 |
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International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 600.121; |
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no |
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Admin @ si @ BKB2018b |
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3174 |
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Author |
Pau Riba; Alicia Fornes; Josep Llados |
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Title |
Towards the Alignment of Handwritten Music Scores |
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Book Chapter |
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Year |
2017 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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103-116 |
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Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment |
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Abstract |
It is very common to nd dierent versions of the same music work in archives of Opera Theaters. These dierences correspond to modications 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 dierences. 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. |
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Bart Lamiroy; R Dueire Lins |
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978-3-319-52158-9 |
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DAG; 600.097; 602.006; 600.121 |
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Admin @ si @ RFL2017 |
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2955 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Title |
Ontology-Based Understanding of Architectural Drawings |
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Book Chapter |
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Year |
2017 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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75-85 |
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Graphics recognition; Floor plan analysi; Domain ontology |
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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. |
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DAG; 600.121 |
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Admin @ si @ HRL2017 |
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3086 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
3D Motion from Image Derivatives using the Least Trimmed Square Regression |
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Book Chapter |
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2006 |
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International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84 |
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Xi'an (China) |
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ADAS @ adas @ DoS2006b |
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690 |
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