| Home | [191–200] << 201 202 203 204 205 206 207 208 209 210 >> [211–220] |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados | ||||
| Title | Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis | Type | Conference Article | ||
| Year | 2022 | Publication | Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 | Abbreviated Journal | |
| Volume | 13424 | Issue | Pages | 336-348 | |
| Keywords | Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk | ||||
| Abstract | Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. | ||||
| Address | June 7-9, 2022, Las Palmas de Gran Canaria, Spain | ||||
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 | IGS | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ BFC2022 | Serial | 3738 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Asma Bensalah; Cristina Carmona_Duarte; Jialuo Chen; Miguel A. Ferrer; Andreas Fischer; Josep Llados; Cristina Martin; Eloy Opisso; Rejean Plamondon; Anna Scius-Bertrand; Josep Maria Tormos | ||||
| Title | The RPM3D Project: 3D Kinematics for Remote Patient Monitoring | Type | Conference Article | ||
| Year | 2022 | Publication | Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 | Abbreviated Journal | |
| Volume | 13424 | Issue | Pages | 217-226 | |
| Keywords | Healthcare applications; Kinematic; Theory of Rapid Human Movements; Human activity recognition; Stroke rehabilitation; 3D kinematics | ||||
| Abstract | This project explores the feasibility of remote patient monitoring based on the analysis of 3D movements captured with smartwatches. We base our analysis on the Kinematic Theory of Rapid Human Movement. We have validated our research in a real case scenario for stroke rehabilitation at the Guttmann Institute (https://www.guttmann.com/en/) (neurorehabilitation hospital), showing promising results. Our work could have a great impact in remote healthcare applications, improving the medical efficiency and reducing the healthcare costs. Future steps include more clinical validation, developing multi-modal analysis architectures (analysing data from sensors, images, audio, etc.), and exploring the application of our technology to monitor other neurodegenerative diseases. | ||||
| Address | June 7-9, 2022, Las Palmas de Gran Canaria, Spain | ||||
| 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 | IGS | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ FBC2022 | Serial | 3739 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network | Type | Conference Article | ||
| Year | 2022 | Publication | Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) | Abbreviated Journal | |
| Volume | 13639 | Issue | Pages | 171-184 | |
| Keywords | Object detection; Optical music recognition; Graph neural network | ||||
| Abstract | During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. | ||||
| Address | December 04 – 07, 2022; Hyderabad, India | ||||
| 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 | ICFHR | ||
| Notes | DAG; 600.162; 600.140; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ BRF2022b | Serial | 3740 | ||
| Permanent link to this record | |||||
| Author | Carlos Boned Riera; Oriol Ramos Terrades | ||||
| Title | Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2186-2191 | ||
| Keywords | Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition | ||||
| Abstract | Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. | ||||
| Address | Montreal; Quebec; Canada; August 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 | ICPR | ||
| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number | Admin @ si @ BoR2022 | Serial | 3741 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa | ||||
| Title | A Generative Model for Guided Thermal Image Super-Resolution | Type | Conference Article | ||
| Year | 2024 | Publication | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | This paper presents a novel approach for thermal super-resolution based on a fusion prior, low-resolution thermal image and H brightness channel of the corresponding visible spectrum image. The method combines bicubic interpolation of the ×8 scale target image with the brightness component. To enhance the guidance process, the original RGB image is converted to HSV, and the brightness channel is extracted. Bicubic interpolation is then applied to the low-resolution thermal image, resulting in a Bicubic-Brightness channel blend. This luminance-bicubic fusion is used as an input image to help the training process. With this fused image, the cyclic adversarial generative network obtains high-resolution thermal image results. Experimental evaluations show that the proposed approach significantly improves spatial resolution and pixel intensity levels compared to other state-of-the-art techniques, making it a promising method to obtain high-resolution thermal. | ||||
| Address | Roma; Italia; February 2024 | ||||
| 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 | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SuS2024 | Serial | 4002 | ||
| Permanent link to this record | |||||
| Author | Hector Laria Mantecon; Kai Wang; Joost Van de Weijer; Bogdan Raducanu; Kai Wang | ||||
| Title | NeRF-Diffusion for 3D-Consistent Face Generation and Editing | Type | Conference Article | ||
| Year | 2024 | Publication | 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Generating high-fidelity 3D-aware images without 3D supervision is a valuable capability in various applications. Current methods based on NeRF features, SDF information, or triplane features have limited variation after training. To address this, we propose a novel approach that combines pretrained models for shape and content generation. Our method leverages a pretrained Neural Radiance Field as a shape prior and a diffusion model for content generation. By conditioning the diffusion model with 3D features, we enhance its ability to generate novel views with 3D awareness. We introduce a consistency token shared between the NeRF module and the diffusion model to maintain 3D consistency during sampling. Moreover, our framework allows for text editing of 3D-aware image generation, enabling users to modify the style over 3D views while preserving semantic content. Our contributions include incorporating 3D awareness into a text-to-image model, addressing identity consistency in 3D view synthesis, and enabling text editing of 3D-aware image generation. We provide detailed explanations, including the shape prior based on the NeRF model and the content generation process using the diffusion model. We also discuss challenges such as shape consistency and sampling saturation. Experimental results demonstrate the effectiveness and visual quality of our approach. | ||||
| Address | Roma; Italia; February 2024 | ||||
| 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 | LAMP | Approved | no | ||
| Call Number | Admin @ si @ LWW2024 | Serial | 4003 | ||
| Permanent link to this record | |||||
| Author | Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera | ||||
| Title | Deep learning with self-supervision and uncertainty regularization to count fish in underwater images | Type | Journal Article | ||
| Year | 2022 | Publication | PloS One | Abbreviated Journal | Plos |
| Volume | 17 | Issue | 5 | Pages | e0267759 |
| Keywords | |||||
| Abstract | Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data. | ||||
| Address | |||||
| Corporate Author |
Thesis | ||||
| Publisher | Public Library of Science | 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 | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ TCC2022 | Serial | 3743 | ||
| Permanent link to this record | |||||
| Author | Yecong Wan; Yuanshuo Cheng; Miingwen Shao; Jordi Gonzalez | ||||
| Title | Image rain removal and illumination enhancement done in one go | Type | Journal Article | ||
| Year | 2022 | Publication | Knowledge-Based Systems | Abbreviated Journal | KBS |
| Volume | 252 | Issue | Pages | 109244 | |
| Keywords | |||||
| Abstract | Rain removal plays an important role in the restoration of degraded images. Recently, CNN-based methods have achieved remarkable success. However, these approaches neglect that the appearance of real-world rain is often accompanied by low light conditions, which will further degrade the image quality, thereby hindering the restoration mission. Therefore, it is very indispensable to jointly remove the rain and enhance illumination for real-world rain image restoration. To this end, we proposed a novel spatially-adaptive network, dubbed SANet, which can remove the rain and enhance illumination in one go with the guidance of degradation mask. Meanwhile, to fully utilize negative samples, a contrastive loss is proposed to preserve more natural textures and consistent illumination. In addition, we present a new synthetic dataset, named DarkRain, to boost the development of rain image restoration algorithms in practical scenarios. DarkRain not only contains different degrees of rain, but also considers different lighting conditions, and more realistically simulates real-world rainfall scenarios. SANet is extensively evaluated on the proposed dataset and attains new state-of-the-art performance against other combining methods. Moreover, after a simple transformation, our SANet surpasses existing the state-of-the-art algorithms in both rain removal and low-light image enhancement. | ||||
| Address | Sept 2022 | ||||
| Corporate Author |
Thesis | ||||
| Publisher | Elsevier | 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; 600.157; 600.168 | Approved | no | ||
| Call Number | Admin @ si @ WCS2022 | Serial | 3744 | ||
| Permanent link to this record | |||||
| Author | Lu Yu; Xialei Liu; Joost Van de Weijer | ||||
| Title | Self-Training for Class-Incremental Semantic Segmentation | Type | Journal Article | ||
| Year | 2022 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | |||
| Keywords | Class-incremental learning; Self-training; Semantic segmentation. | ||||
| Abstract | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous 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 | LAMP; 600.147; 611.008; | Approved | no | ||
| Call Number | Admin @ si @ YLW2022 | Serial | 3745 | ||
| Permanent link to this record | |||||
| Author | Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil | ||||
| Title | EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment | Type | Journal Article | ||
| Year | 2024 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 24 | Issue | 4 | Pages | 1174 |
| Keywords | |||||
| Abstract | High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models. | ||||
| 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 | Approved | no | ||
| Call Number | Admin @ si @ HYF2024 | Serial | 4019 | ||
| Permanent link to this record | |||||
| Author | Victor M. Campello; Carlos Martin-Isla; Cristian Izquierdo; Andrea Guala; Jose F. Rodriguez Palomares; David Vilades; Martin L. Descalzo; Mahir Karakas; Ersin Cavus; Zahra Zahra Raisi-Estabragh; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Karim Lekadir | ||||
| Title | Minimising multi-centre radiomics variability through image normalisation: a pilot study | Type | Journal Article | ||
| Year | 2022 | Publication | Scientific Reports | Abbreviated Journal | ScR |
| Volume | 12 | Issue | 1 | Pages | 12532 |
| Keywords | |||||
| Abstract | Radiomics is an emerging technique for the quantification of imaging data that has recently shown great promise for deeper phenotyping of cardiovascular disease. Thus far, the technique has been mostly applied in single-centre studies. However, one of the main difficulties in multi-centre imaging studies is the inherent variability of image characteristics due to centre differences. In this paper, a comprehensive analysis of radiomics variability under several image- and feature-based normalisation techniques was conducted using a multi-centre cardiovascular magnetic resonance dataset. 218 subjects divided into healthy (n = 112) and hypertrophic cardiomyopathy (n = 106, HCM) groups from five different centres were considered. First and second order texture radiomic features were extracted from three regions of interest, namely the left and right ventricular cavities and the left ventricular myocardium. Two methods were used to assess features’ variability. First, feature distributions were compared across centres to obtain a distribution similarity index. Second, two classification tasks were proposed to assess: (1) the amount of centre-related information encoded in normalised features (centre identification) and (2) the generalisation ability for a classification model when trained on these features (healthy versus HCM classification). The results showed that the feature-based harmonisation technique ComBat is able to remove the variability introduced by centre information from radiomic features, at the expense of slightly degrading classification performance. Piecewise linear histogram matching normalisation gave features with greater generalisation ability for classification ( balanced accuracy in between 0.78 ± 0.08 and 0.79 ± 0.09). Models trained with features from images without normalisation showed the worst performance overall ( balanced accuracy in between 0.45 ± 0.28 and 0.60 ± 0.22). In conclusion, centre-related information removal did not imply good generalisation ability for classification. | ||||
| Address | 2022/07/22 | ||||
| Corporate Author |
Thesis | ||||
| Publisher | Springer Nature | 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 | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ CMI2022 | Serial | 3749 | ||
| Permanent link to this record | |||||
| Author | Ana Garcia Rodriguez; Yael Tudela; Henry Cordova; S. Carballal; I. Ordas; L. Moreira; E. Vaquero; O. Ortiz; L. Rivero; F. Javier Sanchez; Miriam Cuatrecasas; Maria Pellise; Jorge Bernal; Gloria Fernandez Esparrach | ||||
| Title | In vivo computer-aided diagnosis of colorectal polyps using white light endoscopy | Type | Journal Article | ||
| Year | 2022 | Publication | Endoscopy International Open | Abbreviated Journal | ENDIO |
| Volume | 10 | Issue | 9 | Pages | E1201-E1207 |
| Keywords | |||||
| Abstract | Background and study aims Artificial intelligence is currently able to accurately predict the histology of colorectal polyps. However, systems developed to date use complex optical technologies and have not been tested in vivo. The objective of this study was to evaluate the efficacy of a new deep learning-based optical diagnosis system, ATENEA, in a real clinical setting using only high-definition white light endoscopy (WLE) and to compare its performance with endoscopists. Methods ATENEA was prospectively tested in real life on consecutive polyps detected in colorectal cancer screening colonoscopies at Hospital Clínic. No images were discarded, and only WLE was used. The in vivo ATENEA's prediction (adenoma vs non-adenoma) was compared with the prediction of four staff endoscopists without specific training in optical diagnosis for the study purposes. Endoscopists were blind to the ATENEA output. Histology was the gold standard. Results Ninety polyps (median size: 5 mm, range: 2-25) from 31 patients were included of which 69 (76.7 %) were adenomas. ATENEA correctly predicted the histology in 63 of 69 (91.3 %, 95 % CI: 82 %-97 %) adenomas and 12 of 21 (57.1 %, 95 % CI: 34 %-78 %) non-adenomas while endoscopists made correct predictions in 52 of 69 (75.4 %, 95 % CI: 60 %-85 %) and 20 of 21 (95.2 %, 95 % CI: 76 %-100 %), respectively. The global accuracy was 83.3 % (95 % CI: 74%-90 %) and 80 % (95 % CI: 70 %-88 %) for ATENEA and endoscopists, respectively. Conclusion ATENEA can accurately be used for in vivo characterization of colorectal polyps, enabling the endoscopist to make direct decisions. ATENEA showed a global accuracy similar to that of endoscopists despite an unsatisfactory performance for non-adenomatous lesions. | ||||
| Address | 2022 Sep 14 | ||||
| Corporate Author |
Thesis | ||||
| Publisher | PMID | 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; 600.157 | Approved | no | ||
| Call Number | Admin @ si @ GTC2022b | Serial | 3752 | ||
| Permanent link to this record | |||||
| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Paloma Aliende; Monica N. Ramsey | ||||
| Title | Automated detection and classification of multi-cell Phytoliths using Deep Learning-Based Algorithms | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Archaeological Science | Abbreviated Journal | JArchSci |
| Volume | 148 | Issue | Pages | 105654 | |
| Keywords | |||||
| Abstract | This paper presents an algorithm for automated detection and classification of multi-cell phytoliths, one of the major components of many archaeological and paleoenvironmental deposits. This identification, based on phytolith wave pattern, is made using a pretrained VGG19 deep learning model. This approach has been tested in three key phytolith genera for the study of agricultural origins in Near East archaeology: Avena, Hordeum and Triticum. Also, this classification has been validated at species-level using Triticum boeoticum and dicoccoides images. Due to the diversity of microscopes, cameras and chemical treatments that can influence images of phytolith slides, three types of data augmentation techniques have been implemented: rotation of the images at 45-degree angles, random colour and brightness jittering, and random blur/sharpen. The implemented workflow has resulted in an overall accuracy of 93.68% for phytolith genera, improving previous attempts. The algorithm has also demonstrated its potential to automatize the classification of phytoliths species with an overall accuracy of 100%. The open code and platforms employed to develop the algorithm assure the method's accessibility, reproducibility and reusability. | ||||
| Address | December 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 | |||
| Notes | MSIAU; MACO; 600.167 | Approved | no | ||
| Call Number | Admin @ si @ BOL2022 | Serial | 3753 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro | ||||
| Title | Reading Music Systems: From Deep Optical Music Recognition to Contextual Methods | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | The transcription of sheet music into some machine-readable format can be carried out manually. However, the complexity of music notation inevitably leads to burdensome software for music score editing, which makes the whole process
very time-consuming and prone to errors. Consequently, automatic transcription systems for musical documents represent interesting tools. Document analysis is the subject that deals with the extraction and processing of documents through image and pattern recognition. It is a branch of computer vision. Taking music scores as source, the field devoted to address this task is known as Optical Music Recognition (OMR). Typically, an OMR system takes an image of a music score and automatically extracts its content into some symbolic structure such as MEI or MusicXML. In this dissertation, we have investigated different methods for recognizing a single staff section (e.g. scores for violin, flute, etc.), much in the same way as most text recognition research focuses on recognizing words appearing in a given line image. These methods are based in two different methodologies. On the one hand, we present two methods based on Recurrent Neural Networks, in particular, the Long Short-Term Memory Neural Network. On the other hand, a method based on Sequence to Sequence models is detailed. Music context is needed to improve the OMR results, just like language models and dictionaries help in handwriting recognition. For example, syntactical rules and grammars could be easily defined to cope with the ambiguities in the rhythm. In music theory, for example, the time signature defines the amount of beats per bar unit. Thus, in the second part of this dissertation, different methodologies have been investigated to improve the OMR recognition. We have explored three different methods: (a) a graphic tree-structure representation, Dendrograms, that joins, at each level, its primitives following a set of rules, (b) the incorporation of Language Models to model the probability of a sequence of tokens, and (c) graph neural networks to analyze the music scores to avoid meaningless relationships between music primitives. Finally, to train all these methodologies, and given the method-specificity of the datasets in the literature, we have created four different music datasets. Two of them are synthetic with a modern or old handwritten appearance, whereas the other two are real handwritten scores, being one of them modern and the other old. |
||||
| Address | |||||
| Corporate Author |
Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Alicia Fornes | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-124793-8-6 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; | Approved | no | ||
| Call Number | Admin @ si @ Bar2022 | Serial | 3754 | ||
| Permanent link to this record | |||||
| Author | Ali Furkan Biten | ||||
| Title | A Bitter-Sweet Symphony on Vision and Language: Bias and World Knowledge | Type | Book Whole | ||
| Year | 2022 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Vision and Language are broadly regarded as cornerstones of intelligence. Even though language and vision have different aims – language having the purpose of communication, transmission of information and vision having the purpose of constructing mental representations around us to navigate and interact with objects – they cooperate and depend on one another in many tasks we perform effortlessly. This reliance is actively being studied in various Computer Vision tasks, e.g. image captioning, visual question answering, image-sentence retrieval, phrase grounding, just to name a few. All of these tasks share the inherent difficulty of the aligning the two modalities, while being robust to language
priors and various biases existing in the datasets. One of the ultimate goal for vision and language research is to be able to inject world knowledge while getting rid of the biases that come with the datasets. In this thesis, we mainly focus on two vision and language tasks, namely Image Captioning and Scene-Text Visual Question Answering (STVQA). In both domains, we start by defining a new task that requires the utilization of world knowledge and in both tasks, we find that the models commonly employed are prone to biases that exist in the data. Concretely, we introduce new tasks and discover several problems that impede performance at each level and provide remedies or possible solutions in each chapter: i) We define a new task to move beyond Image Captioning to Image Interpretation that can utilize Named Entities in the form of world knowledge. ii) We study the object hallucination problem in classic Image Captioning systems and develop an architecture-agnostic solution. iii) We define a sub-task of Visual Question Answering that requires reading the text in the image (STVQA), where we highlight the limitations of current models. iv) We propose an architecture for the STVQA task that can point to the answer in the image and show how to combine it with classic VQA models. v) We show how far language can get us in STVQA and discover yet another bias which causes the models to disregard the image while doing Visual Question Answering. |
||||
| Address | |||||
| Corporate Author |
Thesis | Ph.D. thesis | |||
| Publisher | IMPRIMA | Place of Publication | Editor | Dimosthenis Karatzas;Lluis Gomez | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-124793-5-5 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Bit2022 | Serial | 3755 | ||
| Permanent link to this record | |||||