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| Records | |||||
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| Author | Rafael E. Rivadeneira; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Multi-Image Super-Resolution for Thermal Images | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Conference on Computer Vision Theory and Applications (VISAPP 2022) | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 635-642 | |
| Keywords | Thermal Images; Multi-view; Multi-frame; Super-Resolution; Deep Learning; Attention Block | ||||
| Abstract | This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two
attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches. |
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Address  |
Online; Feb 6-8, 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 | VISAPP | ||
| Notes | MSIAU; 601.349 | Approved | no | ||
| Call Number | Admin @ si @ RSV2022a | Serial | 3690 | ||
| Permanent link to this record | |||||
| Author | T. Alejandra Vidal; Andrew J. Davison; Juan Andrade; David W. Murray | ||||
| Title | Active Control for Single Camera SLAM | Type | Miscellaneous | ||
| Year | 2006 | Publication | IEEE International Conference on Robotics and Automation, 1930–1936 | Abbreviated Journal | |
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| Address |
Orlando (Florida) | ||||
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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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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | DAG @ dag @ VDA2006 | Serial | 666 | ||
| Permanent link to this record | |||||
| Author | Miquel Ferrer; Ernest Valveny; F. Serratosa; Horst Bunke | ||||
| Title | Exact Median Graph Computation via Graph Embedding | Type | Conference Article | ||
| Year | 2008 | Publication | 12th International Workshop on Structural and Syntactic Pattern Recognition | Abbreviated Journal | |
| Volume | 5324 | Issue | Pages | 15–24 | |
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| Address |
Orlando – Florida (USA) | ||||
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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 | SSPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ FVS2008b | Serial | 1076 | ||
| Permanent link to this record | |||||
| Author | Ekaterina Zaytseva; Jordi Vitria | ||||
| Title | A search based approach to non maximum suppression in face detection | Type | Conference Article | ||
| Year | 2012 | Publication | 19th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Poster
paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results. |
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| Address |
Orlando; USA; September 2012 | ||||
| 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 | 1522-4880 | ISBN | 978-1-4673-2534-9 | Medium | |
| Area | Expedition | Conference | ICIP | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ ZaV2012 | Serial | 2060 | ||
| Permanent link to this record | |||||
| Author | Kamal Nasrollahi; Sergio Escalera; P. Rasti; Gholamreza Anbarjafari; Xavier Baro; Hugo Jair Escalante; Thomas B. Moeslund | ||||
| Title | Deep Learning based Super-Resolution for Improved Action Recognition | Type | Conference Article | ||
| Year | 2015 | Publication | 5th International Conference on Image Processing Theory, Tools and Applications IPTA2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 67 - 72 | ||
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| Abstract | Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos. | ||||
| Address |
Orleans; France; November 2015 | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IPTA | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ NER2015 | Serial | 2648 | ||
| Permanent link to this record | |||||
| Author | Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva | ||||
| Title | Cascade analysis for intestinal contraction detection | Type | Conference Article | ||
| Year | 2006 | Publication | 20th International Congress and exhibition Computer Assisted Radiology and Surgery | Abbreviated Journal | |
| Volume | Issue | Pages | 9-10 | ||
| Keywords | intestine video analysis, anisotropic features, support vector machine, cascade of classifiers | ||||
| Abstract | In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions. | ||||
| Address |
Osaka (Japan) | ||||
| Corporate Author | Thesis | ||||
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| Language | Summary Language | Original Title | |||
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| Area | 800 | Expedition | Conference | CARS | |
| Notes | MV;OR;MILAB;SIAI | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h | Serial | 726 | ||
| Permanent link to this record | |||||
| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Impact of Image Preprocessing Methods on Polyp Localization in Colonoscopy Frames | Type | Conference Article | ||
| Year | 2013 | Publication | 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society | Abbreviated Journal | |
| Volume | Issue | Pages | 7350 - 7354 | ||
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| Abstract | In this paper we present our image preprocessing methods as a key part of our automatic polyp localization scheme. These methods are used to assess the impact of different endoluminal scene elements when characterizing polyps. More precisely we tackle the influence of specular highlights, blood vessels and black mask surrounding the scene. Experimental results prove that the appropriate handling of these elements leads to a great improvement in polyp localization results. | ||||
| Address |
Osaka; Japan; July 2013 | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1557-170X | ISBN | Medium | ||
| Area | 800 | Expedition | Conference | EMBC | |
| Notes | MV; 600.047; 600.060;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSV2013 | Serial | 2286 | ||
| Permanent link to this record | |||||
| Author | Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar | ||||
| Title | Self-Supervised Visual Representations for Cross-Modal Retrieval | Type | Conference Article | ||
| Year | 2019 | Publication | ACM International Conference on Multimedia Retrieval | Abbreviated Journal | |
| Volume | Issue | Pages | 182–186 | ||
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| Abstract | Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset. | ||||
| Address |
Otawa; Canada; june 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICMR | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ PGR2019 | Serial | 3288 | ||
| Permanent link to this record | |||||
| Author | Christian Keilstrup Ingwersen; Artur Xarles; Albert Clapes; Meysam Madadi; Janus Nortoft Jensen; Morten Rieger Hannemose; Anders Bjorholm Dahl; Sergio Escalera | ||||
| Title | Video-based Skill Assessment for Golf: Estimating Golf Handicap | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages | 31-39 | ||
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| Abstract | Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf. | ||||
| Address |
Otawa; Canada; October 2023 | ||||
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| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ KXC2023 | Serial | 3929 | ||
| Permanent link to this record | |||||
| Author | Siyang Song; Micol Spitale; Cheng Luo; German Barquero; Cristina Palmero; Sergio Escalera; Michel Valstar; Tobias Baur; Fabien Ringeval; Elisabeth Andre; Hatice Gunes | ||||
| Title | REACT2023: The First Multiple Appropriate Facial Reaction Generation Challenge | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 31st ACM International Conference on Multimedia | Abbreviated Journal | |
| Volume | Issue | Pages | 9620–9624 | ||
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| Abstract | The Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions. The goal of the challenge is to provide the first benchmark test set for multi-modal information processing and to foster collaboration among the audio, visual, and audio-visual behaviour analysis and behaviour generation (a.k.a generative AI) communities, to compare the relative merits of the approaches to automatic appropriate facial reaction generation under different spontaneous dyadic interaction conditions. This paper presents: (i) the novelties, contributions and guidelines of the REACT2023 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2023. | ||||
| Address |
Otawa; Canada; October 2023 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | MM | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ SSL2023 | Serial | 3931 | ||
| Permanent link to this record | |||||
| Author | Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes | ||||
| Title | ASTRA: An Action Spotting TRAnsformer for Soccer Videos | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
| Volume | Issue | Pages | 93–102 | ||
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| Abstract | In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. | ||||
| Address |
Otawa; Canada; October 2023 | ||||
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| Area | Expedition | Conference | MMSports | ||
| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ XEM2023 | Serial | 3970 | ||
| Permanent link to this record | |||||
| Author | Jaume Amores; Petia Radeva | ||||
| Title | Elastic Matching and Retrieval of IVUS Images Using Contextual Information | Type | Miscellaneous | ||
| Year | 2003 | Publication | 6th Catalan Conference on Artificial Intelligence (CCIA 2003), 137–148 | Abbreviated Journal | |
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| Address |
Palma de Mallorca | ||||
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| Notes | ADAS;MILAB | Approved | no | ||
| Call Number | ADAS @ adas @ AmR2003a | Serial | 364 | ||
| Permanent link to this record | |||||
| Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell | ||||
| Title | High-Level Clothes Description Based on Colour-Texture and Structural Features | Type | Conference Article | ||
| Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | |
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| Address |
Palma de Mallorca | ||||
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| Notes | DAG;CIC | Approved | no | ||
| Call Number | CAT @ cat @ BTL2003b | Serial | 369 | ||
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| Author | David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva | ||||
| Title | Ultrasound to magnetic resonance volume registration for brain sinking measurement | Type | Miscellaneous | ||
| Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | |
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| Address |
Palma de Mallorca | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ LSL2003b | Serial | 391 | ||
| Permanent link to this record | |||||
| Author | David Masip; Jordi Vitria | ||||
| Title | On the Nearest Neighbor Approach for Gender Recognition | Type | Miscellaneous | ||
| Year | 2003 | Publication | 6th Catalonian Conference on Artificial Intelligence | Abbreviated Journal | |
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| Address |
Palma de Mallorca | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MaV2003c | Serial | 392 | ||
| Permanent link to this record | |||||