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| Author | Juan J. Villanueva | ||||
Title  |
Visualization, Imaging, and Image Processing, | Type | Book Whole | ||
| Year | 2008 | Publication | Proceedings of the Eight IASTED International Conference | Abbreviated Journal | |
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| Address | Palma de Mallorca (Spain) | ||||
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| ISSN | ISBN | 978-0-88986-759-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | ISE @ ise @ Vil2008 | Serial | 1003 | ||
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| Author | Juan J. Villanueva | ||||
| Title |
Visualization, Imaging and Image Processing. | Type | Book Whole | ||
| Year | 2002 | Publication | International Association of Science and Technology for Development. ACTA Press, | Abbreviated Journal | |
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| ISSN | ISBN | 0–88986–354–3 | Medium | ||
| Area | Expedition | Conference | IASTE | ||
| Notes | Approved | no | |||
| Call Number | ISE @ ise @ Vil2002 | Serial | 276 | ||
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| Author | Ferran Poveda | ||||
| Title |
Visualització i interpretació tridimensional de l’arquitectura de les fibres musculars del miocardi | Type | Report | ||
| Year | 2009 | Publication | Master en Tecnologies Multimedia | Abbreviated Journal | |
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | 08193 Bellaterra, Barcelona (Spain) | Editor | ||
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| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ Pov2009 | Serial | 1625 | ||
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| Author | Eric Amiel | ||||
| Title |
Visualisation de vaisseaux sanguins | Type | Report | ||
| Year | 2005 | Publication | Rapport de Stage | Abbreviated Journal | |
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| Corporate Author | Université Paul Sabatier Toulouse III | Thesis | Bachelor's thesis | ||
| Publisher | Université Paul Sabatier Toulouse III | Place of Publication | Toulouse | Editor | Enric Marti |
| Language | French | Summary Language | French | Original Title | |
| Series Editor | IUP Systèmes Intelligents | Series Title | Abbreviated Series Title | ||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ Ami2005 | Serial | 1690 | ||
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| Author | Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu | ||||
| Title |
Visual Transformers with Primal Object Queries for Multi-Label Image Classification | Type | Conference Article | ||
| Year | 2022 | Publication | 26th International Conference on Pattern Recognition | Abbreviated Journal | |
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| Abstract | Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively. | ||||
| Address | Montreal; Quebec; Canada; August 2022 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | LAMP; 600.147; 601.309 | Approved | no | ||
| Call Number | Admin @ si @ YWY2022 | Serial | 3786 | ||
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| Author | X. Varona; A. Pujol; Juan J. Villanueva | ||||
| Title |
Visual tracking in application domains. | Type | Miscellaneous | ||
| Year | 1999 | Publication | Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. | Abbreviated Journal | |
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| Address | Bilbao | ||||
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ VPV1999 | Serial | 10 | ||
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| Author | X. Varona; A. Pujol; Juan J. Villanueva | ||||
| Title |
Visual Tracking in Application Domains. | Type | Miscellaneous | ||
| Year | 2000 | Publication | Pattern Recognition and Applications, IOS Press, 99–106. | Abbreviated Journal | |
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| Notes | Approved | no | |||
| Call Number | ISE @ ise @ VPV2000 | Serial | 333 | ||
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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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| 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 | German Ros | ||||
| Title |
Visual SLAM for Driverless Cars: An Initial Survey | Type | Report | ||
| Year | 2012 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 170 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Ros2012c | Serial | 2414 | ||
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| Author | German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez | ||||
| Title |
Visual SLAM for Driverless Cars: A Brief Survey | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | SLAM | ||||
| Abstract | |||||
| Address | Alcalá de Henares | ||||
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| Area | Expedition | Conference | IVW | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RSP2012; ADAS @ adas | Serial | 2019 | ||
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| Author | David Aldavert | ||||
| Title |
Visual Simultaneous Localization and Mapping | Type | Report | ||
| Year | 2006 | Publication | CVC Technical Report #98 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Ald2006 | Serial | 736 | ||
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| Author | German Ros | ||||
| Title |
Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What | Type | Book Whole | ||
| Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating. |
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| Address | |||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa;Julio Guerrero;Antonio Lopez | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-945373-1-8 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Ros2016 | Serial | 2860 | ||
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| Author | Carola Figueroa Flores | ||||
| Title |
Visual Saliency for Object Recognition, and Object Recognition for Visual Saliency | Type | Book Whole | ||
| Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | computer vision; visual saliency; fine-grained object recognition; convolutional neural networks; images classification | ||||
| Abstract | For humans, the recognition of objects is an almost instantaneous, precise and
extremely adaptable process. Furthermore, we have the innate capability to learn new object classes from only few examples. The human brain lowers the complexity of the incoming data by filtering out part of the information and only processing those things that capture our attention. This, mixed with our biological predisposition to respond to certain shapes or colors, allows us to recognize in a simple glance the most important or salient regions from an image. This mechanism can be observed by analyzing on which parts of images subjects place attention; where they fix their eyes when an image is shown to them. The most accurate way to record this behavior is to track eye movements while displaying images. Computational saliency estimation aims to identify to what extent regions or objects stand out with respect to their surroundings to human observers. Saliency maps can be used in a wide range of applications including object detection, image and video compression, and visual tracking. The majority of research in the field has focused on automatically estimating saliency maps given an input image. Instead, in this thesis, we set out to incorporate saliency maps in an object recognition pipeline: we want to investigate whether saliency maps can improve object recognition results. In this thesis, we identify several problems related to visual saliency estimation. First, to what extent the estimation of saliency can be exploited to improve the training of an object recognition model when scarce training data is available. To solve this problem, we design an image classification network that incorporates saliency information as input. This network processes the saliency map through a dedicated network branch and uses the resulting characteristics to modulate the standard bottom-up visual characteristics of the original image input. We will refer to this technique as saliency-modulated image classification (SMIC). In extensive experiments on standard benchmark datasets for fine-grained object recognition, we show that our proposed architecture can significantly improve performance, especially on dataset with scarce training data. Next, we address the main drawback of the above pipeline: SMIC requires an explicit saliency algorithm that must be trained on a saliency dataset. To solve this, we implement a hallucination mechanism that allows us to incorporate the saliency estimation branch in an end-to-end trained neural network architecture that only needs the RGB image as an input. A side-effect of this architecture is the estimation of saliency maps. In experiments, we show that this architecture can obtain similar results on object recognition as SMIC but without the requirement of ground truth saliency maps to train the system. Finally, we evaluated the accuracy of the saliency maps that occur as a sideeffect of object recognition. For this purpose, we use a set of benchmark datasets for saliency evaluation based on eye-tracking experiments. Surprisingly, the estimated saliency maps are very similar to the maps that are computed from human eye-tracking experiments. Our results show that these saliency maps can obtain competitive results on benchmark saliency maps. On one synthetic saliency dataset this method even obtains the state-of-the-art without the need of ever having seen an actual saliency image for training. |
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| Address | March 2021 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Joost Van de Weijer;Bogdan Raducanu | |
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| ISSN | ISBN | 978-84-122714-4-7 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ Fig2021 | Serial | 3600 | ||
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| Author | David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras | ||||
| Title |
Visual Registration Method For A Low Cost Robot: Computer Vision Systems | Type | Conference Article | ||
| Year | 2009 | Publication | 7th International Conference on Computer Vision Systems | Abbreviated Journal | |
| Volume | 5815 | Issue | Pages | 204–214 | |
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| Abstract | An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. | ||||
| Address | Belgica | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-04666-7 | Medium | |
| Area | Expedition | Conference | ICVS | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ ATR2009b | Serial | 1247 | ||
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| Author | A. Martinez; Jordi Vitria; J. Lopez | ||||
| Title |
Visual Recognition of Surroundings: A robot that knows where it is. | Type | Miscellaneous | ||
| Year | 1997 | Publication | Intelligence Artificielle et Complexite. | Abbreviated Journal | |
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| Address | Paris. | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MVL1997 | Serial | 59 | ||
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