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Author Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli
Title (up) Batch-based activity recognition from egocentric photo-streams revisited Type Journal Article
Year 2018 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 21 Issue 4 Pages 953–965
Keywords Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks
Abstract Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ CMR2018 Serial 3186
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Author Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria
Title (up) Bayesian Classification for Inspection of Industrial Products. Type Miscellaneous
Year 2002 Publication Abbreviated Journal
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Abstract
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ RBT2002a Serial 285
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Author Petia Radeva; M. Bressan; A. Tovar; Jordi Vitria
Title (up) Bayesian Classification for Inspection of Industrial Products. Type Miscellaneous
Year 2002 Publication Abbreviated Journal
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Series Editor Series Title Abbreviated Series Title
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ RBT2002c Serial 316
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Author Jordi Vitria; M. Bressan; Petia Radeva
Title (up) Bayesian classification of cork stoppers using class-conditional independent component analysis Type Journal
Year 2006 Publication IEEE Transactions on Systems, Man and Cybernetics (Part C), 36(6) Abbreviated Journal
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VBR2006 Serial 723
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Author Jordi Vitria; M. Bressan; Petia Radeva
Title (up) Bayesian classification of cork stoppers using class-conditional independent component analysis Type Journal
Year 2007 Publication IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ VBR2007 Serial 795
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Author Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva
Title (up) Bayesian deep learning for semantic segmentation of food images Type Journal Article
Year 2022 Publication Computers and Electrical Engineering Abbreviated Journal CEE
Volume 103 Issue Pages 108380
Keywords Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis
Abstract Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images.
Address October 2022
Corporate Author Thesis
Publisher Science Direct 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
Notes MILAB Approved no
Call Number Admin @ si @ ANR2022 Serial 3763
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Author X. Orriols; Andrew Willis; X. Binefa; David B. Cooper
Title (up) Bayesian estimation of axial symmetries from partial data, a generative model approach Type Report
Year 2000 Publication CVC Technical Report #49 Abbreviated Journal
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Address CVC (UAB)
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Notes Approved no
Call Number DAG @ dag @ OWB2000 Serial 536
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Author Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski
Title (up) Bayesian Flow Networks in Continual Learning Type Miscellaneous
Year 2023 Publication arxiv Abbreviated Journal
Volume Issue Pages
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Abstract Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data.
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Notes LAMP Approved no
Call Number Admin @ si @ PDT2023 Serial 3972
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Author Lluis Barcelo; X. Binefa
Title (up) Bayesian Video Mosaicing with moving objects Type Journal
Year 2002 Publication International Journal of Pattern Recognition and Artificial Intelligence, 16(3): 341–348 (IF: 0.359) Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ BaB2002 Serial 268
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Author Lluis Barcelo; X. Binefa
Title (up) Bayesian Video Mosaicing with Moving Objects. Type Miscellaneous
Year 2001 Publication Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:91–96. Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ BaB2001 Serial 72
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Author Zhijie Fang
Title (up) Behavior understanding of vulnerable road users by 2D pose estimation Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
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Abstract Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians
and cyclists can be critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, therefore, should be taken into account by systems providing any level of driving assistance, i.e. from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this PhD work, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow the established traffic codes to indicate future left/right turns and stop maneuvers with arm signals. In the case of pedestrians, no indications can be assumed a priori. Instead, we hypothesize that the walking pattern of a pedestrian can allow us to determine if he/she has the intention of crossing the road in the path of the egovehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this PhD work, we show how the same methodology can be used for recognizing pedestrians and cyclists’ intentions. For pedestrians, we perform experiments on the publicly available Daimler and JAAD datasets. For cyclists, we did not found an analogous dataset, therefore, we created our own one by acquiring
and annotating corresponding video-sequences which we aim to share with the
research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.
Address May 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Antonio Lopez;David Vazquez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-948531-6-6 Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ Fan2019 Serial 3388
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Author Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva
Title (up) Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams Type Conference Article
Year 2020 Publication ECCV Workshops Abbreviated Journal
Volume 12538 Issue Pages 469-484
Keywords
Abstract The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.
Address Virtual; August 2020
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 ECCVW
Notes MILAB; no proj Approved no
Call Number Admin @ si @ MTM2020 Serial 3528
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Author Weiqing Min; Shuqiang Jiang; Jitao Sang; Huayang Wang; Xinda Liu; Luis Herranz
Title (up) Being a Supercook: Joint Food Attributes and Multimodal Content Modeling for Recipe Retrieval and Exploration Type Journal Article
Year 2017 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM
Volume 19 Issue 5 Pages 1100 - 1113
Keywords
Abstract This paper considers the problem of recipe-oriented image-ingredient correlation learning with multi-attributes for recipe retrieval and exploration. Existing methods mainly focus on food visual information for recognition while we model visual information, textual content (e.g., ingredients), and attributes (e.g., cuisine and course) together to solve extended recipe-oriented problems, such as multimodal cuisine classification and attribute-enhanced food image retrieval. As a solution, we propose a multimodal multitask deep belief network (M3TDBN) to learn joint image-ingredient representation regularized by different attributes. By grouping ingredients into visible ingredients (which are visible in the food image, e.g., “chicken” and “mushroom”) and nonvisible ingredients (e.g., “salt” and “oil”), M3TDBN is capable of learning both midlevel visual representation between images and visible ingredients and nonvisual representation. Furthermore, in order to utilize different attributes to improve the intermodality correlation, M3TDBN incorporates multitask learning to make different attributes collaborate each other. Based on the proposed M3TDBN, we exploit the derived deep features and the discovered correlations for three extended novel applications: 1) multimodal cuisine classification; 2) attribute-augmented cross-modal recipe image retrieval; and 3) ingredient and attribute inference from food images. The proposed approach is evaluated on the constructed Yummly dataset and the evaluation results have validated the effectiveness of the proposed approach.
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Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ MJS2017 Serial 2964
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Author German Barquero; Sergio Escalera; Cristina Palmero
Title (up) BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction Type Conference Article
Year 2023 Publication IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
Volume Issue Pages 2317-2327
Keywords
Abstract Stochastic human motion prediction (HMP) has generally been tackled with generative adversarial networks and variational autoencoders. Most prior works aim at predicting highly diverse movements in terms of the skeleton joints’ dispersion. This has led to methods predicting fast and motion-divergent movements, which are often unrealistic and incoherent with past motion. Such methods also neglect contexts that need to anticipate diverse low-range behaviors, or actions, with subtle joint displacements. To address these issues, we present BeLFusion, a model that, for the first time, leverages latent diffusion models in HMP to sample from a latent space where behavior is disentangled from pose and motion. As a result, diversity is encouraged from a behavioral perspective. Thanks to our behavior
coupler’s ability to transfer sampled behavior to ongoing motion, BeLFusion’s predictions display a variety of behaviors that are significantly more realistic than the state of the art. To support it, we introduce two metrics, the Area of
the Cumulative Motion Distribution, and the Average Pairwise Distance Error, which are correlated to our definition of realism according to a qualitative study with 126 participants. Finally, we prove BeLFusion’s generalization power in a new cross-dataset scenario for stochastic HMP.
Address 2-6 October 2023. Paris (France)
Corporate Author Thesis
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ICCV
Notes HUPBA; no menciona Approved no
Call Number Admin @ si @ BEP2023 Serial 3829
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Author E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara
Title (up) Benchmarking Keypoint Filtering Approaches for Document Image Matching Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy.
Address Kyoto; Japan; November 2017
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 ICDAR
Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ RCR2017 Serial 3000
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