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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title (down) Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation Type Journal Article
Year 2012 Publication IEEE Journal of Selected Topics in Signal Processing Abbreviated Journal J-STSP
Volume 6 Issue 5 Pages 437-446
Keywords
Abstract This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain.
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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 1932-4553 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ BLS2012b Serial 2155
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Author Fernando Barrera
Title (down) Multimodal Stereo from Thermal Infrared and Visible Spectrum Type Book Whole
Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recent advances in thermal infrared imaging (LWIR) has allowed its use in applications beyond of the military domain. Nowadays, this new family of sensors is included in different technical and scientific applications. They offer features that facilitate tasks, such as detection of pedestrians, hot spots, differences in temperature, among others, which can significantly improve the performance of a system where the persons are expected to play the principal role. For instance, video surveillance applications, monitoring, and pedestrian detection.
During this dissertation the next question is stated: Could a couple of sensors measuring different bands of the electromagnetic spectrum, as the visible and thermal infrared, be used to extract depth information? Although it is a complex question, we shows that a system of these characteristics is possible as well as their advantages, drawbacks, and potential opportunities.
The matching and fusion of data coming from different sensors, as the emissions registered at visible and infrared bands, represents a special challenge, because it has been showed that theses signals are weak correlated. Therefore, many traditional techniques of image processing and computer vision are not helpful, requiring adjustments for their correct performance in every modality.
In this research an experimental study that compares different cost functions and matching approaches is performed, in order to build a multimodal stereovision system. Furthermore, the common problems in infrared/visible stereo, specially in the outdoor scenes are identified. Our framework summarizes the architecture of a generic stereo algorithm, at different levels: computational, functional, and structural, which can be extended toward high-level fusion (semantic) and high-order (prior).The proposed framework is intended to explore novel multimodal stereo matching approaches, going from sparse to dense representations (both disparity and depth maps). Moreover, context information is added in form of priors and assumptions. Finally, this dissertation shows a promissory way toward the integration of multiple sensors for recovering three-dimensional information.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Felipe Lumbreras;Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Bar2012 Serial 2209
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Author Adam Fodor; Rachid R. Saboundji; Julio C. S. Jacques Junior; Sergio Escalera; David Gallardo Pujol; Andras Lorincz
Title (down) Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures Type Conference Article
Year 2022 Publication Understanding Social Behavior in Dyadic and Small Group Interactions Abbreviated Journal
Volume 173 Issue Pages 218-241
Keywords
Abstract Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots.
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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 PMLR
Notes HuPBA; no menciona Approved no
Call Number Admin @ si @ FSJ2022 Serial 3769
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Author David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri
Title (down) Multimodal Registration of Intravascular Ultrasound Images and Angiography. Type Miscellaneous
Year 2002 Publication XX Congreso Anual de la Sociedad Española de Ingenieria Biomedica CASEIB 2002, 1: 137–140. Abbreviated Journal
Volume Issue Pages
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Abstract
Address Zaragoza, Espanya
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
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ RRF2002b Serial 317
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Author David Rotger
Title (down) Multimodal Registration of Intravascular Ultrasound Images and Angiography Type Miscellaneous
Year 2002 Publication Director: P. Radeva, Master Thesis. Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ Rot2002 Serial 324
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Author Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
Title (down) Multimodal page classification in administrative document image streams Type Journal Article
Year 2014 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 17 Issue 4 Pages 331-341
Keywords Digital mail room; Multimodal page classification; Visual and textual document description
Abstract In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
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Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-2833 ISBN Medium
Area Expedition Conference
Notes DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 Approved no
Call Number Admin @ si @ RFK2014 Serial 2523
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Author Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol
Title (down) Multimodal laughter recognition in video conversations Type Conference Article
Year 2009 Publication 2nd IEEE Workshop on CVPR for Human communicative Behavior analysis Abbreviated Journal
Volume Issue Pages 110–115
Keywords
Abstract Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier.
Address Miami (USA)
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 2160-7508 ISBN 978-1-4244-3994-2 Medium
Area Expedition Conference CVPR
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2009c Serial 1188
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Author Miguel Oliveira; Victor Santos; Angel Sappa
Title (down) Multimodal Inverse Perspective Mapping Type Journal Article
Year 2015 Publication Information Fusion Abbreviated Journal IF
Volume 24 Issue Pages 108–121
Keywords Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles
Abstract Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ OSS2015c Serial 2532
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Author Angel Sappa; Jordi Vitria
Title (down) Multimodal Interaction in Image and Video Applications Type Book Whole
Year 2013 Publication Multimodal Interaction in Image and Video Applications Abbreviated Journal
Volume 48 Issue Pages
Keywords
Abstract Book Series Intelligent Systems Reference Library
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1868-4394 ISBN 978-3-642-35931-6 Medium
Area Expedition Conference
Notes ADAS; OR;MV Approved no
Call Number Admin @ si @ SaV2013 Serial 2199
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Author Maya Dimitrova; Ch. Roumenin; Petia Radeva; David Rotger; Juan J. Villanueva
Title (down) Multimodal Intelligent System for Cardiovascular Diagnosis Type Miscellaneous
Year 2003 Publication Automation and Informatics, any XXXVII, num. 3 Abbreviated Journal
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ DRR2003 Serial 374
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Author Henry Velesaca; Gisel Bastidas-Guacho; Mohammad Rouhani; Angel Sappa
Title (down) Multimodal image registration techniques: a comprehensive survey Type Journal Article
Year 2024 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume Issue Pages
Keywords
Abstract This manuscript presents a review of state-of-the-art techniques proposed in the literature for multimodal image registration, addressing instances where images from different modalities need to be precisely aligned in the same reference system. This scenario arises when the images to be registered come from different modalities, among the visible and thermal spectral bands, 3D-RGB, or flash-no flash, or NIR-visible. The review spans different techniques from classical approaches to more modern ones based on deep learning, aiming to highlight the particularities required at each step in the registration pipeline when dealing with multimodal images. It is noteworthy that medical images are excluded from this review due to their specific characteristics, including the use of both active and passive sensors or the non-rigid nature of the body contained in the image.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes MSIAU Approved no
Call Number Admin @ si @ VBR2024 Serial 3997
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Author Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
Title (down) Multimodal grid features and cell pointers for scene text visual question answering Type Journal Article
Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 150 Issue Pages 242-249
Keywords
Abstract This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link.
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GBT2021 Serial 3620
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Author Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier
Title (down) Multimodal First Impression Analysis with Deep Residual Networks Type Journal Article
Year 2018 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC
Volume 8 Issue 3 Pages 316-329
Keywords
Abstract People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ GGB2018 Serial 3210
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Author Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez
Title (down) Multimodal end-to-end autonomous driving Type Journal Article
Year 2020 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume Issue Pages 1-11
Keywords
Abstract A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality.
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Notes ADAS Approved no
Call Number Admin @ si @ XCG2020 Serial 3490
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Author Antonio Lopez
Title (down) Multilocal Methods for Ridge and Valley Delineation in Image Analysis. Type Book Whole
Year 2000 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
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Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Joan Serrat
Language Summary Language Original Title
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Notes ADAS Approved no
Call Number ADAS @ adas @ Lop2000 Serial 174
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