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| Author | Ana Garcia Rodriguez; Jorge Bernal; F. Javier Sanchez; Henry Cordova; Rodrigo Garces Duran; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
| Title | Polyp fingerprint: automatic recognition of colorectal polyps’ unique features | Type | Journal Article | ||
| Year | 2020 | Publication | Surgical Endoscopy and other Interventional Techniques | Abbreviated Journal | SEND |
| Volume | 34 | Issue | 4 | Pages | 1887-1889 |
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| Abstract | BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint'). METHODS: A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset. RESULTS: The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%). CONCLUSIONS: A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition. KEYWORDS: Artificial intelligence; Colorectal polyps; Content-based image retrieval |
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| Notes | MV; no menciona | Approved | no | ||
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Admin @ si @ | Serial | 3403 | ||
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| Author | Aura Hernandez-Sabate; Lluis Albarracin; F. Javier Sanchez | ||||
| Title | Graph-Based Problem Explorer: A Software Tool to Support Algorithm Design Learning While Solving the Salesperson Problem | Type | Journal | ||
| Year | 2020 | Publication | Mathematics | Abbreviated Journal | MATH |
| Volume | 20 | Issue | 8(9) | Pages | 1595 |
| Keywords | STEM education; Project-based learning; Coding; software tool | ||||
| Abstract | In this article, we present a sequence of activities in the form of a project in order to promote
learning on design and analysis of algorithms. The project is based on the resolution of a real problem, the salesperson problem, and it is theoretically grounded on the fundamentals of mathematical modelling. In order to support the students’ work, a multimedia tool, called Graph-based Problem Explorer (GbPExplorer), has been designed and refined to promote the development of computer literacy in engineering and science university students. This tool incorporates several modules to allow coding different algorithmic techniques solving the salesman problem. Based on an educational design research along five years, we observe that working with GbPExplorer during the project provides students with the possibility of representing the situation to be studied in the form of graphs and analyze them from a computational point of view. |
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| Address | September 2020 | ||||
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| Notes | IAM; ISE | Approved | no | ||
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Admin @ si @ | Serial | 3722 | ||
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| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Mahmood Fathy; Sergio Escalera | ||||
| Title | Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation | Type | Conference Article | ||
| Year | 2020 | Publication | Bioimage computation workshop | Abbreviated Journal | |
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| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA | Approved | no | ||
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Admin @ si @ AAF2020 | Serial | 3520 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa | ||||
| Title | Fast CNN Stereo Depth Estimation through Embedded GPU Devices | Type | Journal Article | ||
| Year | 2020 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 20 | Issue | 11 | Pages | 3249 |
| Keywords | stereo matching; deep learning; embedded GPU | ||||
| Abstract | Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices. | ||||
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| Notes | MSIAU; 600.122 | Approved | no | ||
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Admin @ si @ AAN2020 | Serial | 3428 | ||
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| Author | Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas | ||||
| Title | WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval | Type | Book Chapter | ||
| Year | 2020 | Publication | Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis | Abbreviated Journal | |
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| Publisher | Springer | Place of Publication | Editor | Analysis”, K. Alahari; C.V. Jawahar | |
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| Series Editor | Series Title | Series on Advances in Computer Vision and Pattern Recognition | Abbreviated Series Title | ||
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| Notes | DAG; 600.121 | Approved | no | ||
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Admin @ si @ AGG2020 | Serial | 3496 | ||
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| Author | Eduardo Aguilar; Petia Radeva | ||||
| Title | Uncertainty-aware integration of local and flat classifiers for food recognition | Type | Journal Article | ||
| Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 136 | Issue | Pages | 237-243 | |
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| Abstract | Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
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Admin @ si @ AgR2020 | Serial | 3525 | ||
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| Author | Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva | ||||
| Title | Uncertainty Modeling and Deep Learning Applied to Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 13th International Joint Conference on Biomedical Engineering Systems and Technologies | Abbreviated Journal | |
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| Abstract | Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. | ||||
| Address | Villetta; Malta; February 2020 | ||||
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| Area | Expedition | Conference | BIODEVICES | ||
| Notes | MILAB | Approved | no | ||
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Admin @ si @ ANK2020 | Serial | 3526 | ||
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| Author | Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana | ||||
| Title | Saliency from High-Level Semantic Image Features | Type | Journal | ||
| Year | 2020 | Publication | SN Computer Science | Abbreviated Journal | SN |
| Volume | 1 | Issue | 4 | Pages | 1-12 |
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| Abstract | Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS). | ||||
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| Notes | LAMP; 600.120; 600.109; 600.106 | Approved | no | ||
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Admin @ si @ AWD2020 | Serial | 3503 | ||
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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 | International Workshop on Artificial Intelligence for Healthcare Applications | Abbreviated Journal | |
| Volume | 12661 | Issue | Pages | 476-489 | |
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| 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 | ||||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | DAG; 600.121; 600.140; | Approved | no | ||
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Admin @ si @ BCF2020 | Serial | 3508 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Computations of top-down attention by modulating V1 dynamics | Type | Conference Article | ||
| Year | 2020 | Publication | Computational and Mathematical Models in Vision | Abbreviated Journal | |
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| Address | St. Pete Beach; Florida; May 2020 | ||||
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| Area | Expedition | Conference | MODVIS | ||
| Notes | NEUROBIT | Approved | no | ||
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Admin @ si @ BeO2020a | Serial | 3376 | ||
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| Author | David Berga; Xavier Otazu | ||||
| Title | Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1 | Type | Journal Article | ||
| Year | 2020 | Publication | Neurocomputing | Abbreviated Journal | NEUCOM |
| Volume | 417 | Issue | Pages | 270-289 | |
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| Abstract | Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return, oculomotor and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologically inspired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of saccade amplitude and inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts. | ||||
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| Notes | NEUROBIT | Approved | no | ||
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Admin @ si @ BeO2020c | Serial | 3444 | ||
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| Author | Arnau Baro; Alicia Fornes; Carles Badal | ||||
| Title | Handwritten Historical Music Recognition by Sequence-to-Sequence with Attention Mechanism | Type | Conference Article | ||
| Year | 2020 | Publication | 17th International Conference on Frontiers in Handwriting Recognition | Abbreviated Journal | |
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| Abstract | Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. | ||||
| Address | Virtual ICFHR; September 2020 | ||||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.140; 600.121 | Approved | no | ||
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Admin @ si @ BFB2020 | Serial | 3448 | ||
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| Author | Hugo Bertiche; Meysam Madadi; Sergio Escalera | ||||
| Title | CLOTH3D: Clothed 3D Humans | Type | Conference Article | ||
| Year | 2020 | Publication | 16th European Conference on Computer Vision | Abbreviated Journal | |
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| Abstract | This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape. | ||||
| Address | Virtual; August 2020 | ||||
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| Area | Expedition | Conference | ECCV | ||
| Notes | HUPBA | Approved | no | ||
| Call Number |
Admin @ si @ BME2020 | Serial | 3519 | ||
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| Author | David Berga; Marc Masana; Joost Van de Weijer | ||||
| Title | Disentanglement of Color and Shape Representations for Continual Learning | Type | Conference Article | ||
| Year | 2020 | Publication | ICML Workshop on Continual Learning | Abbreviated Journal | |
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| Abstract | We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
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Admin @ si @ BMW2020 | Serial | 3506 | ||
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| Author | Manuel Carbonell | ||||
| Title | Neural Information Extraction from Semi-structured Documents A | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Sectors as fintech, legaltech or insurance process an inflow of millions of forms, invoices, id documents, claims or similar every day. Together with these, historical archives provide gigantic amounts of digitized documents containing useful information that needs to be stored in machine encoded text with a meaningful structure. This procedure, known as information extraction (IE) comprises the steps of localizing and recognizing text, identifying named entities contained in it and optionally finding relationships among its elements. In this work we explore multi-task neural models at image and graph level to solve all steps in a unified way. While doing so we find benefits and limitations of these end-to-end approaches in comparison with sequential separate methods. More specifically, we first propose a method to produce textual as well as semantic labels with a unified model from handwritten text line images. We do so with the use of a convolutional recurrent neural model trained with connectionist temporal classification to predict the textual as well as semantic information encoded in the images. Secondly, motivated by the success of this approach we investigate the unification of the localization and recognition tasks of handwritten text in full pages with an end-to-end model, observing benefits in doing so. Having two models that tackle information extraction subsequent task pairs in an end-to-end to end manner, we lastly contribute with a method to put them all together in a single neural network to solve the whole information extraction pipeline in a unified way. Doing so we observe some benefits and some limitations in the approach, suggesting that in certain cases it is beneficial to train specialized models that excel at a single challenging task of the information extraction process, as it can be the recognition of named entities or the extraction of relationships between them. For this reason we lastly study the use of the recently arrived graph neural network architectures for the semantic tasks of the information extraction process, which are recognition of named entities and relation extraction, achieving promising results on the relation extraction part. | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Alicia Fornes;Mauricio Villegas;Josep Llados | |
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| ISSN | ISBN | 978-84-122714-1-6 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number |
Admin @ si @ Car20 | Serial | 3483 | ||
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