| Records |
| Author |
Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros |
| Title |
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
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Issue |
13 |
Pages |
243-258 |
| Keywords |
Domain Adaptation |
| Abstract |
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. |
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Springer |
Place of Publication |
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Editor |
Gabriela Csurka |
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| Notes |
ADAS; 600.085; 601.223; 600.076; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ LXG2017 |
Serial |
2872 |
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| Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
| Title |
Vision-Based Advanced Driver Assistance Systems |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Computer Vision in Vehicle Technology: Land, Sea, and Air |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
ADAS; Autonomous Driving |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ GVE2017 |
Serial |
2881 |
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| Author |
German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
| Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
12 |
Issue |
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Pages |
227-241 |
| Keywords |
SYNTHIA; Virtual worlds; Autonomous Driving |
| Abstract |
Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
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Springer |
Place of Publication |
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Editor |
Gabriela Csurka |
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| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 |
Approved |
no |
| Call Number |
ADAS @ adas @ RSV2017 |
Serial |
2882 |
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| Author |
Marçal Rusiñol; Josep Llados |
| Title |
Flowchart Recognition in Patent Information Retrieval |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Current Challenges in Patent Information Retrieval |
Abbreviated Journal |
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| Volume |
37 |
Issue |
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Pages |
351-368 |
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Springer Berlin Heidelberg |
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Editor |
M. Lupu; K. Mayer; N. Kando; A.J. Trippe |
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| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ RuL2017 |
Serial |
2896 |
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| Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
| Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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| Volume |
9657 |
Issue |
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Pages |
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Springer |
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Editor |
B. Lamiroy; R Dueire Lins |
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LNCS |
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Conference |
GREC |
| Notes |
DAG; 600.097; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ JLR2017 |
Serial |
2928 |
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| Author |
H. Martin Kjer; Jens Fagertun; Sergio Vera; Debora Gil |
| Title |
Medial structure generation for registration of anatomical structures |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Skeletonization, Theory, Methods and Applications |
Abbreviated Journal |
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| Volume |
11 |
Issue |
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Pages |
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| Notes |
IAM; 600.096; 600.075; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ MFV2017a |
Serial |
2935 |
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| Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
| Title |
Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary |
Type |
Book Chapter |
| Year |
2016 |
Publication |
Recent Trends in Image Processing and Pattern Recognition |
Abbreviated Journal |
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| Volume |
709 |
Issue |
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Conference |
RTIP2R |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ HTR2016 |
Serial |
2956 |
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| Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera |
| Title |
Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey |
Type |
Book Chapter |
| Year |
2017 |
Publication |
Gesture Recognition |
Abbreviated Journal |
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Issue |
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Pages |
539-578 |
| Keywords |
Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies |
| Abstract |
Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research. |
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| Notes |
HUPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ACB2017a |
Serial |
2981 |
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| Author |
Hans Stadthagen-Gonzalez; Luis Lopez; M. Carmen Parafita; C. Alejandro Parraga |
| Title |
Using two-alternative forced choice tasks and Thurstone law of comparative judgments for code-switching research |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Linguistic Approaches to Bilingualism |
Abbreviated Journal |
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Issue |
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Pages |
67-97 |
| Keywords |
two-alternative forced choice and Thurstone's law; acceptability judgment; code-switching |
| Abstract |
This article argues that 2-alternative forced choice tasks and Thurstone’s law of comparative judgments (Thurstone, 1927) are well suited to investigate code-switching competence by means of acceptability judgments. We compare this method with commonly used Likert scale judgments and find that the 2-alternative forced choice task provides granular details that remain invisible in a Likert scale experiment. In order to compare and contrast both methods, we examined the syntactic phenomenon usually referred to as the Adjacency Condition (AC) (apud Stowell, 1981), which imposes a condition of adjacency between verb and object. Our interest in the AC comes from the fact that it is a subtle feature of English grammar which is absent in Spanish, and this provides an excellent springboard to create minimal code-switched pairs that allow us to formulate a clear research question that can be tested using both methods. |
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NEUROBIT; no menciona |
Approved |
no |
| Call Number |
Admin @ si @ SLP2018 |
Serial |
2994 |
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| Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
| Title |
Challenges in Multi-modal Gesture Recognition |
Type |
Book Chapter |
| Year |
2017 |
Publication |
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Issue |
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Pages |
1-60 |
| Keywords |
Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM |
| Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ EAG2017 |
Serial |
3008 |
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| Author |
Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata |
| Title |
Environmental Perception for Intelligent Vehicles |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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Issue |
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Pages |
23–101 |
| Keywords |
Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles |
| Abstract |
Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @AAA2018 |
Serial |
3046 |
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| Author |
Antonio Lopez; David Vazquez; Gabriel Villalonga |
| Title |
Data for Training Models, Domain Adaptation |
Type |
Book Chapter |
| Year |
2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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Issue |
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Pages |
395–436 |
| Keywords |
Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data |
| Abstract |
Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. |
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| Notes |
ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ LVV2018 |
Serial |
3047 |
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| Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
| Title |
Ontology-Based Understanding of Architectural Drawings |
Type |
Book Chapter |
| Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
Issue |
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75-85 |
| Keywords |
Graphics recognition; Floor plan analysi; Domain ontology |
| Abstract |
In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
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| Notes |
DAG; 600.121 |
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no |
| Call Number |
Admin @ si @ HRL2017 |
Serial |
3086 |
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| Author |
Antonio Lopez |
| Title |
Pedestrian Detection Systems |
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Book Chapter |
| Year |
2018 |
Publication |
Wiley Encyclopedia of Electrical and Electronics Engineering |
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Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. |
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ADAS; 600.118 |
Approved |
no |
| Call Number |
Admin @ si @ Lop2018 |
Serial |
3230 |
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| Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
| Title |
Self-Supervised Learning from Web Data for Multimodal Retrieval |
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Book Chapter |
| Year |
2019 |
Publication |
Multi-Modal Scene Understanding Book |
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279-306 |
| Keywords |
self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding |
| Abstract |
Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
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DAG; 600.129; 601.338; 601.310 |
Approved |
no |
| Call Number |
Admin @ si @ GGG2019 |
Serial |
3266 |
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