| Records |
| Author |
Ekain Artola |
| Title |
Human Attention Map Prediction Combining Visual Features |
Type |
Report |
| Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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| Volume |
160 |
Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
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| Corporate Author |
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Thesis |
Bachelor's thesis |
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Approved |
no |
Call Number  |
Admin @ si @ Art2010 |
Serial |
1352 |
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| Author |
Cristhian Aguilera; M.Ramos; Angel Sappa |
| Title |
Simulated Annealing: A Novel Application of Image Processing in the Wood Area |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Simulated Annealing – Advances, Applications and Hybridizations |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
91-104 |
| Keywords |
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Editor |
Marcos de Sales Guerra Tsuzuki |
| Language |
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Original Title |
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| Series Editor |
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Series Title |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-953-51-0710-1 |
Medium |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ ARS2012 |
Serial |
2156 |
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| Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
| Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops -Visual Continual Learning workshop |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3444-3454 |
| Keywords |
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| Abstract |
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
| Address |
Paris; France; October 2023 |
| Corporate Author |
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Conference |
ICCVW |
| Notes |
LAMP; MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ARR2023 |
Serial |
3841 |
| Permanent link to this record |
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| Author |
Eduardo Aguilar; Bogdan Raducanu; Petia Radeva; Joost Van de Weijer |
| Title |
Continual Evidential Deep Learning for Out-of-Distribution Detection |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3444-3454 |
| Keywords |
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| Abstract |
Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-ofdistribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method 1, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95. |
| Address |
Paris; France; October 2023 |
| Corporate Author |
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Edition |
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Medium |
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Expedition |
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Conference |
ICCVW |
| Notes |
LAMP; MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ARR2023 |
Serial |
3974 |
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| Author |
Joan Arnedo-Moreno; Agata Lapedriza |
| Title |
Visualizing key authenticity: turning your face into your public key |
Type |
Conference Article |
| Year |
2010 |
Publication |
6th China International Conference on Information Security and Cryptology |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
605-618 |
| Keywords |
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| Abstract |
Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process. |
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LNCS |
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Conference |
Inscrypt |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ ArL2010c |
Serial |
2149 |
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| Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Real-time Object Segmentation using a Bag of Features Approach |
Type |
Conference Article |
| Year |
2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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| Volume |
220 |
Issue |
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Pages |
321–329 |
| Keywords |
Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors |
| Abstract |
In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. |
| Address |
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Thesis |
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| Publisher |
IOS Press Amsterdam, |
Place of Publication |
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Editor |
In R.Alquezar, A.Moreno, J.Aguilar. |
| Language |
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Original Title |
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| Series Editor |
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Edition |
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ISBN |
9781607506423 |
Medium |
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Expedition |
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Conference |
CCIA |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ ARL2010b |
Serial |
1417 |
| Permanent link to this record |
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| Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1046–1053 |
| Keywords |
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| Abstract |
We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
| Address |
San Francisco; CA; USA; June 2010 |
| Corporate Author |
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Place of Publication |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ ARL2010a |
Serial |
1311 |
| Permanent link to this record |
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| Author |
Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva |
| Title |
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants |
Type |
Journal Article |
| Year |
2018 |
Publication |
IEEE Transactions on Multimedia |
Abbreviated Journal |
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| Volume |
20 |
Issue |
12 |
Pages |
3266 - 3275 |
| Keywords |
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| Abstract |
The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. |
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Conference |
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| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ARB2018 |
Serial |
3236 |
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| Author |
Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
| Title |
Can biological solutions help computers detect symmetry? |
Type |
Conference Article |
| Year |
2017 |
Publication |
40th European Conference on Visual Perception |
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| Address |
Berlin; Germany; August 2017 |
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Conference |
ECVP |
| Notes |
NEUROBIT |
Approved |
no |
| Call Number |
Admin @ si @ APE2017 |
Serial |
2995 |
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| Author |
Juan Andrade; F. Thomas |
| Title |
Wire-Based Tracking using Mutual Information |
Type |
Miscellaneous |
| Year |
2006 |
Publication |
10th International Symposium on Advances in Robot Kinematics, 3–14 |
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| Address |
Ljubljana (Slovenia) |
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Approved |
no |
| Call Number |
Admin @ si @ AnT2006 |
Serial |
665 |
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| Author |
Juan Andrade; A. Sanfeliu |
| Title |
The effects of partial observability when building fully correlated maps |
Type |
Journal |
| Year |
2005 |
Publication |
IEEE Transactions on Robotics, 21(4):771–777 (IF: 1.486) |
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Approved |
no |
| Call Number |
Admin @ si @ AnS2005 |
Serial |
594 |
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| Author |
Eduardo Aguilar; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva |
| Title |
Bayesian deep learning for semantic segmentation of food images |
Type |
Journal Article |
| Year |
2022 |
Publication |
Computers and Electrical Engineering |
Abbreviated Journal |
CEE |
| Volume |
103 |
Issue |
|
Pages |
108380 |
| Keywords |
Deep learning; Uncertainty quantification; Bayesian inference; Image segmentation; Food analysis |
| Abstract |
Deep learning has provided promising results in various applications; however, algorithms tend to be overconfident in their predictions, even though they may be entirely wrong. Particularly for critical applications, the model should provide answers only when it is very sure of them. This article presents a Bayesian version of two different state-of-the-art semantic segmentation methods to perform multi-class segmentation of foods and estimate the uncertainty about the given predictions. The proposed methods were evaluated on three public pixel-annotated food datasets. As a result, we can conclude that Bayesian methods improve the performance achieved by the baseline architectures and, in addition, provide information to improve decision-making. Furthermore, based on the extracted uncertainty map, we proposed three measures to rank the images according to the degree of noisy annotations they contained. Note that the top 135 images ranked by one of these measures include more than half of the worst-labeled food images. |
| Address |
October 2022 |
| Corporate Author |
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Thesis |
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| Publisher |
Science Direct |
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| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ANR2022 |
Serial |
3763 |
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| Author |
Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva |
| Title |
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis |
Type |
Conference Article |
| Year |
2020 |
Publication |
13th International Joint Conference on Biomedical Engineering Systems and Technologies |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. |
| Address |
Villetta; Malta; February 2020 |
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BIODEVICES |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ANK2020 |
Serial |
3526 |
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| Author |
Gholamreza Anbarjafari; Sergio Escalera |
| Title |
Human-Robot Interaction: Theory and Application |
Type |
Book Whole |
| Year |
2018 |
Publication |
Human-Robot Interaction: Theory and Application |
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ISBN |
978-1-78923-316-2 |
Medium |
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Conference |
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| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ AnE2018 |
Serial |
3216 |
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| Author |
Jaume Amores |
| Title |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
| Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
| Volume |
42 |
Issue |
2 |
Pages |
381-407 |
| Keywords |
Multi-instance learning; Codebook; Bag of words |
| Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
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| Publisher |
Springer London |
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Edition |
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| ISSN |
0219-1377 |
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| Notes |
ADAS; 601.042; 600.057; 600.076 |
Approved |
no |
| Call Number |
Admin @ si @ Amo2015 |
Serial |
2383 |
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