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| Author | Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera | ||||
| Title | Introducing the Separability Matrix for Error Correcting Output Codes Coding | Type | Conference Article | ||
| Year | 2011 | Publication | 10th International conference on Multiple Classifier Systems | Abbreviated Journal | |
| Volume | 6713 | Issue | Pages | 227-236 | |
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| Abstract | Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results. | ||||
| Address | Napoles, Italy | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Editor | Carlo Sansone; Josef Kittler; Fabio Roli | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-642-21556-8 | Medium | ||
| Area | Expedition | Conference | MCS | ||
| Notes | MILAB; OR;HuPBA;MV | Approved | no | ||
Call Number  |
Admin @ si @ BPB2011a | Serial | 1771 | ||
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| 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 | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.121; 600.162 | Approved | no | ||
| Call Number |
Admin @ si @ BoR2022 | Serial | 3741 | ||
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| Author | Marc Bolaños; Petia Radeva | ||||
| Title | Simultaneous Food Localization and Recognition | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images. |
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| Address | Cancun; Mexico; December 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number |
Admin @ si @ BoR2016 | Serial | 2834 | ||
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| Author | Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J Gerrits; Jonas Gregorio de Souza; Afifa Khan; Maria Suarez Moreno; Jack Tomaney; Rebecca C Roberts; Cameron A Petrie | ||||
| Title | Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan | Type | Journal Article | ||
| Year | 2023 | Publication | Scientific Reports | Abbreviated Journal | ScR |
| Volume | 13 | Issue | Pages | 11257 | |
| Keywords | |||||
| Abstract | This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number |
Admin @ si @ BOL2023 | Serial | 3976 | ||
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| 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 | |
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| 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 | ||||
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| Area | Expedition | Conference | |||
| Notes | MSIAU; MACO; 600.167 | Approved | no | ||
| Call Number |
Admin @ si @ BOL2022 | Serial | 3753 | ||
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| Author | Xavier Boix | ||||
| Title | Learning Conditional Random Fields for Stereo | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 136 | Issue | Pages | ||
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| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Bellaterra, Barcelona | Editor | ||
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| Notes | CIC | Approved | no | ||
| Call Number |
Admin @ si @ Boi2009 | Serial | 2395 | ||
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| Author | David Berga; Xavier Otazu; Xose R. Fernandez-Vidal; Victor Leboran; Xose M. Pardo | ||||
| Title | Generating Synthetic Images for Visual Attention Modeling | Type | Journal Article | ||
| Year | 2019 | Publication | Perception | Abbreviated Journal | PER |
| Volume | 48 | Issue | Pages | 99 | |
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| Area | Expedition | Conference | |||
| Notes | NEUROBIT; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ BOF2019 | Serial | 3309 | ||
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| Author | Jorge Bernal; Joan M. Nuñez; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation | Type | Conference Article | ||
| Year | 2014 | Publication | 3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging | Abbreviated Journal | |
| Volume | 8680 | Issue | Pages | 41-49 | |
| Keywords | Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps | ||||
| Abstract | In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation. | ||||
| Address | Boston; USA; September 2014 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CLIP | ||
| Notes | MV; 600.060; 600.044; 600.047;SIAI | Approved | no | ||
| Call Number |
Admin @ si @ BNS2014 | Serial | 2502 | ||
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| Author | Nil Ballus; Bhalaji Nagarajan; Petia Radeva | ||||
| Title | Opt-SSL: An Enhanced Self-Supervised Framework for Food Recognition | Type | Conference Article | ||
| Year | 2022 | Publication | 10th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 13256 | Issue | Pages | ||
| Keywords | Self-supervised; Contrastive learning; Food recognition | ||||
| Abstract | Self-supervised Learning has been showing upbeat performance in several computer vision tasks. The popular contrastive methods make use of a Siamese architecture with different loss functions. In this work, we go deeper into two very recent state of the art frameworks, namely, SimSiam and Barlow Twins. Inspired by them, we propose a new self-supervised learning method we call Opt-SSL that combines both image and feature contrasting. We validate the proposed method on the food recognition task, showing that our proposed framework enables the self-learning networks to learn better visual representations. | ||||
| Address | Aveiro; Portugal; May 2022 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ BNR2022 | Serial | 3782 | ||
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| Author | German Barquero; Johnny Nuñez; Sergio Escalera; Zhen Xu; Wei-Wei Tu; Isabelle Guyon | ||||
| Title | Didn’t see that coming: a survey on non-verbal social human behavior forecasting | Type | Conference Article | ||
| Year | 2022 | Publication | Understanding Social Behavior in Dyadic and Small Group Interactions | Abbreviated Journal | |
| Volume | 173 | Issue | Pages | 139-178 | |
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| Abstract | Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises
methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues. |
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| Address | Virtual; June 2022 | ||||
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| Area | Expedition | Conference | PMLR | ||
| Notes | HuPBA; no proj | Approved | no | ||
| Call Number |
Admin @ si @ BNE2022 | Serial | 3766 | ||
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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 | |
| Volume | Issue | Pages | |||
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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 | ||
| Call Number |
Admin @ si @ BMW2020 | Serial | 3506 | ||
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| Author | Gisel Bastidas-Guacho; Patricio Moreno; Boris X. Vintimilla; Angel Sappa | ||||
| Title | Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches | Type | Conference Article | ||
| Year | 2023 | Publication | 13th International Conference on Pattern Recognition Systems | Abbreviated Journal | |
| Volume | 14234 | Issue | Pages | 25–36 | |
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| Abstract | Multimodal image fusion allows the combination of information from different modalities, which is useful for tasks such as object detection, edge detection, and tracking, to name a few. Using the fused representation for applications results in better task performance. There are several image fusion approaches, which have been summarized in surveys. However, the existing surveys focus on image fusion approaches where the application on the loop of multimodal image fusion is not considered. On the contrary, this study summarizes deep learning-based multimodal image fusion for computer vision (e.g., object detection) and image processing applications (e.g., semantic segmentation), that is, approaches where the application module leverages the multimodal fusion process to enhance the final result. Firstly, we introduce image fusion and the existing general frameworks for image fusion tasks such as multifocus, multiexposure and multimodal. Then, we describe the multimodal image fusion approaches. Next, we review the state-of-the-art deep learning multimodal image fusion approaches for vision applications. Finally, we conclude our survey with the trends of task-driven multimodal image fusion. | ||||
| Address | Guayaquil; Ecuador; July 2023 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPRS | ||
| Notes | MSIAU | Approved | no | ||
| Call Number |
Admin @ si @ BMV2023 | Serial | 3932 | ||
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| Author | Hugo Bertiche; Meysam Madadi; Emilio Tylson; Sergio Escalera | ||||
| Title | DeePSD: Automatic Deep Skinning And Pose Space Deformation For 3D Garment Animation | Type | Conference Article | ||
| Year | 2021 | Publication | 19th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 5471-5480 | ||
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| Abstract | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | ||||
| Address | Virtual; October 2021 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number |
Admin @ si @ BMT2021 | Serial | 3606 | ||
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| Author | Marc Bolaños; R. Mestre; Estefania Talavera; Xavier Giro; Petia Radeva | ||||
| Title | Visual Summary of Egocentric Photostreams by Representative Keyframes | Type | Conference Article | ||
| Year | 2015 | Publication | IEEE International Conference on Multimedia and Expo ICMEW2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
| Keywords | egocentric; lifelogging; summarization; keyframes | ||||
| Abstract | Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted bymeans of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the
summaries. |
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| Address | Torino; italy; July 2015 | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | 978-1-4799-7079-7 | Edition | ||
| ISSN | ISBN | 978-1-4799-7079-7 | Medium | ||
| Area | Expedition | Conference | ICME | ||
| Notes | MILAB | Approved | no | ||
| Call Number |
Admin @ si @ BMT2015 | Serial | 2638 | ||
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| Author | Xavier Baro; David Masip; Elena Planas; Julia Minguillon | ||||
| Title | PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación | Type | Miscellaneous | ||
| Year | 2013 | Publication | XV Jornadas de Enseñanza Universitaria de la Informatica | Abbreviated Journal | |
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| Area | Expedition | Conference | JENUI | ||
| Notes | OR;HuPBA;MV | Approved | no | ||
| Call Number |
Admin @ si @ BMP2013 | Serial | 2237 | ||
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