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Author |
Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl |
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Title |
The EMPATHIC Virtual Coach: a demo |
Type |
Conference Article |
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Year |
2021 |
Publication |
23rd ACM International Conference on Multimodal Interaction |
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Pages |
848-851 |
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Abstract |
The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios. |
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Address |
Virtual; October 2021 |
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ICMI |
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Notes |
HUPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ OVB2021 |
Serial |
3644 |
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Author |
Aymen Azaza |
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Title |
Context, Motion and Semantic Information for Computational Saliency |
Type |
Book Whole |
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Year |
2018 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
The main objective of this thesis is to highlight the salient object in an image or in a video sequence. We address three important—but in our opinion
insufficiently investigated—aspects of saliency detection. Firstly, we start
by extending previous research on saliency which explicitly models the information provided from the context. Then, we show the importance of
explicit context modelling for saliency estimation. Several important works
in saliency are based on the usage of object proposals. However, these methods
focus on the saliency of the object proposal itself and ignore the context.
To introduce context in such saliency approaches, we couple every object
proposal with its direct context. This allows us to evaluate the importance
of the immediate surround (context) for its saliency. We propose several
saliency features which are computed from the context proposals including
features based on omni-directional and horizontal context continuity. Secondly,
we investigate the usage of top-downmethods (high-level semantic
information) for the task of saliency prediction since most computational
methods are bottom-up or only include few semantic classes. We propose
to consider a wider group of object classes. These objects represent important
semantic information which we will exploit in our saliency prediction
approach. Thirdly, we develop a method to detect video saliency by computing
saliency from supervoxels and optical flow. In addition, we apply the
context features developed in this thesis for video saliency detection. The
method combines shape and motion features with our proposed context
features. To summarize, we prove that extending object proposals with their
direct context improves the task of saliency detection in both image and
video data. Also the importance of the semantic information in saliency
estimation is evaluated. Finally, we propose a newmotion feature to detect
saliency in video data. The three proposed novelties are evaluated on standard
saliency benchmark datasets and are shown to improve with respect to
state-of-the-art. |
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Address |
October 2018 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
Place of Publication |
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Editor |
Joost Van de Weijer;Ali Douik |
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ISBN |
978-84-945373-9-4 |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ Aza2018 |
Serial |
3218 |
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Author |
Lu Yu; Yongmei Cheng; Joost Van de Weijer |
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Title |
Weakly Supervised Domain-Specific Color Naming Based on Attention |
Type |
Conference Article |
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Year |
2018 |
Publication |
24th International Conference on Pattern Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
3019 - 3024 |
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Abstract |
The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains. |
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Address |
Beijing; August 2018 |
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Conference |
ICPR |
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Notes |
LAMP; 600.109; 602.200; 600.120 |
Approved |
no |
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Call Number |
Admin @ si @ YCW2018 |
Serial |
3243 |
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Permanent link to this record |
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Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
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Title |
Automatic Image-Based Waste Classification |
Type |
Conference Article |
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Year |
2019 |
Publication |
International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning |
Abbreviated Journal |
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Volume |
11487 |
Issue |
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Pages |
422–431 |
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Keywords |
Computer Vision; Deep learning; Convolutional neural networks; Waste classification |
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Abstract |
The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. |
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Address |
Almeria; June 2019 |
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LNCS |
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Conference |
IWINAC |
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Notes |
LAMP; 600.120 |
Approved |
no |
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Call Number |
RSV2019 |
Serial |
3273 |
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Permanent link to this record |
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Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
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Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
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Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
Abbreviated Journal |
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Volume |
11047 |
Issue |
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Pages |
34-42 |
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Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
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Abstract |
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
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Address |
Cuba; September 2018 |
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IWAIPR |
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Notes |
MILAB; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
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Permanent link to this record |
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Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Title |
Visible-Thermal Fusion based Monocular Visual Odometry |
Type |
Conference Article |
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Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
Abbreviated Journal |
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Volume |
417 |
Issue |
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Pages |
517-528 |
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Keywords |
Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
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Abstract |
The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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Address |
Lisboa; Portugal; November 2015 |
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Publisher |
Springer International Publishing |
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ISSN |
2194-5357 |
ISBN |
978-3-319-27145-3 |
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Conference |
ROBOT |
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Notes |
ADAS; 600.076; 600.086 |
Approved |
no |
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Call Number |
Admin @ si @ PAD2015 |
Serial |
2663 |
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Permanent link to this record |
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Author |
Aitor Alvarez-Gila |
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Title |
Self-supervised learning for image-to-image translation in the small data regime |
Type |
Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
Computer vision; Neural networks; Self-supervised learning; Image-to-image mapping; Probabilistic programming |
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Abstract |
The mass irruption of Deep Convolutional Neural Networks (CNNs) in computer vision since 2012 led to a dominance of the image understanding paradigm consisting in an end-to-end fully supervised learning workflow over large-scale annotated datasets. This approach proved to be extremely useful at solving a myriad of classic and new computer vision tasks with unprecedented performance —often, surpassing that of humans—, at the expense of vast amounts of human-labeled data, extensive computational resources and the disposal of all of our prior knowledge on the task at hand. Even though simple transfer learning methods, such as fine-tuning, have achieved remarkable impact, their success when the amount of labeled data in the target domain is small is limited. Furthermore, the non-static nature of data generation sources will often derive in data distribution shifts that degrade the performance of deployed models. As a consequence, there is a growing demand for methods that can exploit elements of prior knowledge and sources of information other than the manually generated ground truth annotations of the images during the network training process, so that they can adapt to new domains that constitute, if not a small data regime, at least a small labeled data regime. This thesis targets such few or no labeled data scenario in three distinct image-to-image mapping learning problems. It contributes with various approaches that leverage our previous knowledge of different elements of the image formation process: We first present a data-efficient framework for both defocus and motion blur detection, based on a model able to produce realistic synthetic local degradations. The framework comprises a self-supervised, a weakly-supervised and a semi-supervised instantiation, depending on the absence or availability and the nature of human annotations, and outperforms fully-supervised counterparts in a variety of settings. Our knowledge on color image formation is then used to gather input and target ground truth image pairs for the RGB to hyperspectral image reconstruction task. We make use of a CNN to tackle this problem, which, for the first time, allows us to exploit spatial context and achieve state-of-the-art results given a limited hyperspectral image set. In our last contribution to the subfield of data-efficient image-to-image transformation problems, we present the novel semi-supervised task of zero-pair cross-view semantic segmentation: we consider the case of relocation of the camera in an end-to-end trained and deployed monocular, fixed-view semantic segmentation system often found in industry. Under the assumption that we are allowed to obtain an additional set of synchronized but unlabeled image pairs of new scenes from both original and new camera poses, we present ZPCVNet, a model and training procedure that enables the production of dense semantic predictions in either source or target views at inference time. The lack of existing suitable public datasets to develop this approach led us to the creation of MVMO, a large-scale Multi-View, Multi-Object path-traced dataset with per-view semantic segmentation annotations. We expect MVMO to propel future research in the exciting under-developed fields of cross-view and multi-view semantic segmentation. Last, in a piece of applied research of direct application in the context of process monitoring of an Electric Arc Furnace (EAF) in a steelmaking plant, we also consider the problem of simultaneously estimating the temperature and spectral emissivity of distant hot emissive samples. To that end, we design our own capturing device, which integrates three point spectrometers covering a wide range of the Ultra-Violet, visible, and Infra-Red spectra and is capable of registering the radiance signal incoming from an 8cm diameter spot located up to 20m away. We then define a physically accurate radiative transfer model that comprises the effects of atmospheric absorbance, of the optical system transfer function, and of the sample temperature and spectral emissivity themselves. We solve this inverse problem without the need for annotated data using a probabilistic programming-based Bayesian approach, which yields full posterior distribution estimates of the involved variables that are consistent with laboratory-grade measurements. |
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Address |
Julu, 2019 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
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Place of Publication |
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Editor |
Joost Van de Weijer; Estibaliz Garrote |
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Notes |
LAMP |
Approved |
no |
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Call Number |
Admin @ si @ Alv2022 |
Serial |
3716 |
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Permanent link to this record |
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Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Title |
ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences |
Type |
Conference Article |
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Year |
2009 |
Publication |
12th International Conference on Medical Image and Computer Assisted Intervention |
Abbreviated Journal |
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Volume |
5762 |
Issue |
II |
Pages |
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Keywords |
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Abstract |
The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers. |
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Address |
London, UK |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-04270-6 |
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Conference |
MICCAI |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ CPF2009 |
Serial |
1228 |
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Permanent link to this record |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
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Title |
Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces |
Type |
Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
43 |
Issue |
4 |
Pages |
1642–1655 |
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Keywords |
Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces |
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Abstract |
The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed. |
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Elsevier |
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DAG |
Approved |
no |
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Call Number |
DAG @ dag @ FVS2010 |
Serial |
1294 |
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Permanent link to this record |
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Author |
Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; Horst Bunke |
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Title |
A Recursive Embedding Approach to Median Graph Computation |
Type |
Conference Article |
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Year |
2009 |
Publication |
7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition |
Abbreviated Journal |
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Volume |
5534 |
Issue |
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Pages |
113–123 |
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Keywords |
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Abstract |
The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches. |
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Address |
Venice, Italy |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Series Volume |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-02123-7 |
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Conference |
GBR |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ FKV2009 |
Serial |
1173 |
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Permanent link to this record |
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Author |
Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; I. Bardaji; Horst Bunke |
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Title |
A Generic Framework for Median Graph Computation based on a Recursive Embedding Approach |
Type |
Journal Article |
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Year |
2011 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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Volume |
115 |
Issue |
7 |
Pages |
919-928 |
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Keywords |
Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition |
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Abstract |
The median graph has been shown to be a good choice to obtain a represen- tative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previ- ous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set me- dian and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four dif- ferent databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medi- ans, in terms of the sum of distances to the training graphs, than with the previous existing methods. |
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DAG |
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no |
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IAM @ iam @ FKV2011 |
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1831 |
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Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter |
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Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI |
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Conference Article |
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2019 |
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27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine |
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The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease. |
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ISMRM |
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LAMP; 600.120 |
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Admin @ si @ KRS2019 |
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3300 |
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Author |
Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang |
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Title |
An Effective Solution to Double Counting Problem in Human Pose Estimation |
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Miscellaneous |
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2015 |
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Arxiv |
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Pose estimation; double counting problem; mix-ture of parts Model |
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The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.
An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015]. |
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ISE; 600.078 |
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Admin @ si @ GHG2015 |
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2590 |
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Giuseppe Pezzano; Oliver Diaz; Vicent Ribas Ripoll; Petia Radeva |
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CoLe-CNN+: Context learning – Convolutional neural network for COVID-19-Ground-Glass-Opacities detection and segmentation |
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Journal Article |
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2021 |
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Computers in Biology and Medicine |
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CBM |
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136 |
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104689 |
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The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19. |
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MILAB; no menciona |
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no |
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Admin @ si @ PDR2021 |
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3635 |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
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Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
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Conference Article |
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Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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560-568 |
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Pedestrian Detection |
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The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.076; 600.057; 600.054 |
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ADAS @ adas @ GVR2015 |
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2585 |
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