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| Author | Rafael E. Rivadeneira; Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Thermal Image SuperResolution Through Deep Convolutional Neural Network | Type | Conference Article | ||
| Year | 2019 | Publication | 16th International Conference on Images Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages  |
417-426 | ||
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| Abstract | Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.
cidis.espol.edu.ec/es/dataset. |
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| Address | Waterloo; Canada; August 2019 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICIAR | ||
| Notes | MSIAU; 600.130; 601.349; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ RSS2019 | Serial | 3269 | ||
| Permanent link to this record | |||||
| Author | Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari | ||||
| Title | Action recognition using single-pixel time-of-flight detection | Type | Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 4 | Pages |
414 |
| Keywords | single pixel single photon image acquisition; time-of-flight; action recognition | ||||
| Abstract | Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network. |
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ OHC2019 | Serial | 3319 | ||
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| Author | Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades | ||||
| Title | Exploring the impact of inter-query variability on the performance of retrieval systems | Type | Conference Article | ||
| Year | 2014 | Publication | 11th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 8814 | Issue | Pages |
413–420 | |
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| Abstract | This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes. | ||||
| Address | Algarve; Portugal; October 2014 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | 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 | 978-3-319-11757-7 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | IAM; DAG; 600.060; 600.061; 600.077; 600.075 | Approved | no | ||
| Call Number | Admin @ si @ BGB2014 | Serial | 2559 | ||
| Permanent link to this record | |||||
| Author | Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz | ||||
| Title | Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis | Type | Journal Article | ||
| Year | 2015 | Publication | American Journal of Physiology-Gastrointestinal and Liver Physiology | Abbreviated Journal | AJPGI |
| Volume | 309 | Issue | 6 | Pages |
G413--G419 |
| Keywords | capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning | ||||
| Abstract | We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. | ||||
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| Publisher | American Physiological Society | Place of Publication | Editor | ||
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| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MDS2015 | Serial | 2666 | ||
| Permanent link to this record | |||||
| Author | Spencer Low; Oliver Nina; Angel Sappa; Erik Blasch; Nathan Inkawhich | ||||
| Title | Multi-Modal Aerial View Object Classification Challenge Results-PBVS 2023 | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages |
412-421 | ||
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| Abstract | This paper presents the findings and results of the third edition of the Multi-modal Aerial View Object Classification (MAVOC) challenge in a detailed and comprehensive manner. The challenge consists of two tracks. The primary aim of both tracks is to encourage research into building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Participating teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge demonstrated the feasibility of combining both modalities, the 2022 challenge expanded on the capability of multi-modal models. The 2023 challenge introduces a refined version of the UNICORN dataset and demonstrates significant improvements made. The 2023 challenge adopts an updated UNIfied CO-incident Optical and Radar for recognitioN (UNICORN V2) dataset and competition format. Two tasks are featured: SAR classification and SAR + EO classification. In addition to measuring accuracy of models, we also introduce out-of-distribution measures to encourage model robustness.The majority of this paper is dedicated to discussing the top performing methods and evaluating their performance on our blind test set. It is worth noting that all of the top ten teams outperformed the Resnet-50 baseline. The top team for SAR classification achieved a 173% performance improvement over the baseline, while the top team for SAR + EO classification achieved a 175% improvement. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ LNS2023b | Serial | 3915 | ||
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| Author | D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom | ||||
| Title | Automated classification of renal interstitium and tubules by local texture analysis and a neural network | Type | Journal Article | ||
| Year | 1996 | Publication | Analytical and Quantitative Cytology and Histology | Abbreviated Journal | |
| Volume | 18 | Issue | 5 | Pages |
410-9, PMID: 8908314 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SMG1996 | Serial | 76 | ||
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| Author | Reza Azad; Maryam Asadi Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | Type | Conference Article | ||
| Year | 2019 | Publication | Visual Recognition for Medical Images workshop | Abbreviated Journal | |
| Volume | Issue | Pages |
406-415 | ||
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| Abstract | In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance. | ||||
| Address | Seul; Korea; October 2019 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAF2019 | Serial | 3324 | ||
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| Author | Weijia Wu; Yuzhong Zhao; Zhuang Li; Jiahong Li; Mike Zheng Shou; Umapada Pal; Dimosthenis Karatzas; Xiang Bai | ||||
| Title | ICDAR 2023 Competition on Video Text Reading for Dense and Small Text | Type | Conference Article | ||
| Year | 2023 | Publication | 17th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 14188 | Issue | Pages |
405–419 | |
| Keywords | Video Text Spotting; Small Text; Text Tracking; Dense Text | ||||
| Abstract | Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignore extreme video texts challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, named DSText, which focuses on dense and small text reading challenge in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., ‘Game’, ‘Sports’, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task2)). During the competition period (opened on 15th February, 2023 and closed on 20th March, 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise the video text research in the community. | ||||
| Address | San Jose; CA; USA; August 2023 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ WZL2023 | Serial | 3898 | ||
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| Author | Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil | ||||
| Title | Selection of Radiomics Features based on their Reproducibility | Type | Conference Article | ||
| Year | 2019 | Publication | 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society | Abbreviated Journal | |
| Volume | Issue | Pages |
403-408 | ||
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| Abstract | Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. | ||||
| Address | Berlin; Alemanya; July 2019 | ||||
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| Area | Expedition | Conference | EMBC | ||
| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ LTS2019 | Serial | 3358 | ||
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| Author | Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer | ||||
| Title | When Deep Learners Change Their Mind: Learning Dynamics for Active Learning | Type | Conference Article | ||
| Year | 2021 | Publication | 19th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 13052 | Issue | 1 | Pages |
403-413 |
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| Abstract | Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results. | ||||
| Address | September 2021 | ||||
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| Area | Expedition | Conference | CAIP | ||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ ZRV2021 | Serial | 3673 | ||
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| Author | Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | A Holistic Approach for the Detection of Media-Adventitia Border in IVUS | Type | Conference Article | ||
| Year | 2011 | Publication | 14th International Conference on Medical Image Computing and Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 6893 | Issue | Pages |
401-408 | |
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| Abstract | In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm. | ||||
| Address | Toronto, Canada | ||||
| 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 | 978-3-642-23625-9 | Medium | |
| Area | Expedition | Conference | MICCAI | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CPG2011 | Serial | 1739 | ||
| Permanent link to this record | |||||
| Author | Xavier Carrillo; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; Petia Radeva; Neus Salvatella; Oriol Pujol; J. Mauri; A. Bayes | ||||
| Title | Changes in Radial Artery Volume Assessed Using Intravascular Ultrasound: A Comparison of Two Vasodilator Regimens in Transradial Coronary Intervention | Type | Journal Article | ||
| Year | 2011 | Publication | Journal of Invasive Cardiology | Abbreviated Journal | JOIC |
| Volume | 23 | Issue | 10 | Pages |
401-404 |
| Keywords | radial; vasodilator treatment; percutaneous coronary intervention; IVUS; volumetric IVUS analysis | ||||
| Abstract | OBJECTIVES:
This study used intravascular ultrasound (IVUS) to evaluate radial artery volume changes after intraarterial administration of nitroglycerin and/or verapamil. BACKGROUND: Radial artery spasm, which is associated with radial artery size, is the main limitation of the transradial approach in percutaneous coronary interventions (PCI). METHODS: This prospective, randomized study compared the effect of two intra-arterial vasodilator regimens on radial artery volume: 0.2 mg of nitroglycerin plus 2.5 mg of verapamil (Group 1; n = 15) versus 2.5 mg of verapamil alone (Group 2; n = 15). Radial artery lumen volume was assessed using IVUS at two time points: at baseline (5 minutes after sheath insertion) and post-vasodilator (1 minute after drug administration). The luminal volume of the radial artery was computed using ECOC Random Fields (ECOC-RF), a technique used for automatic segmentation of luminal borders in longitudinal cut images from IVUS sequences. RESULTS: There was a significant increase in arterial lumen volume in both groups, with an increase from 451 ± 177 mm³ to 508 ± 192 mm³ (p = 0.001) in Group 1 and from 456 ± 188 mm³ to 509 ± 170 mm³ (p = 0.001) in Group 2. There were no significant differences between the groups in terms of absolute volume increase (58 mm³ versus 53 mm³, respectively; p = 0.65) or in relative volume increase (14% versus 20%, respectively; p = 0.69). CONCLUSIONS: Administration of nitroglycerin plus verapamil or verapamil alone to the radial artery resulted in similar increases in arterial lumen volume according to ECOC-RF IVUS measurements. |
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| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CFC2011 | Serial | 1797 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa | ||||
| Title | Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation | Type | Miscellaneous | ||
| Year | 2006 | Publication | 6th IASTED International Conference on Visualization, Imaging and Image Processing | Abbreviated Journal | VIIP |
| Volume | Issue | Pages |
400–405 | ||
| Keywords | ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms. | ||||
| Abstract | In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows. With this approach, the system can work in urban roads, as will be illustrated by current results. |
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| Address | Palma de Mallorca (Spain) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSL2006 | Serial | 672 | ||
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| Author | Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados | ||||
| Title | Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model | Type | Conference Article | ||
| Year | 2018 | Publication | 13th IAPR International Workshop on Document Analysis Systems | Abbreviated Journal | |
| Volume | Issue | Pages |
399-404 | ||
| Keywords | Named entity recognition; Handwritten Text Recognition; neural networks | ||||
| Abstract | When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing. |
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| Address | Vienna; Austria; April 2018 | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG; 600.097; 603.057; 601.311; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CVF2018 | Serial | 3170 | ||
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| Author | V. Valev; B. Sankur; Petia Radeva | ||||
| Title | Generalized Non Reducible Descriptors. | Type | Conference Article | ||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 2 | Issue | Pages |
397-397 | |
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| Address | Barcelona. | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSR2000 | Serial | 230 | ||
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