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| Records | |||||
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| Author | G. de Oliveira; Mariella Dimiccoli; Petia Radeva | ||||
| Title | Egocentric Image Retrieval With Deep Convolutional Neural Networks | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 71-76 | ||
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| Address | Barcelona; Spain; October 2016 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CCIA | ||
Notes  |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ODR2016 | Serial | 2790 | ||
| Permanent link to this record | |||||
| Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
| Title | With whom do I interact with? Social interaction detection in egocentric photo-streams | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ADR2016a | Serial | 2791 | ||
| Permanent link to this record | |||||
| Author | Mariella Dimiccoli; Petia Radeva | ||||
| Title | Lifelogging in the era of outstanding digitization | Type | Conference Article | ||
| Year | 2015 | Publication | International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage | Abbreviated Journal | |
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| Abstract | In this paper, we give an overview on the emerging trend of the digitized self, focusing on visual lifelogging through wearable cameras. This is about continuously recording our life from a first-person view by wearing a camera that passively captures images. On one hand, visual lifelogging has opened the door to a large number of applications, including health. On the other, it has also boosted new challenges in the field of data analysis as well as new ethical concerns. While currently increasing efforts are being devoted to exploit lifelogging data for the improvement of personal well-being, we believe there are still many interesting applications to explore, ranging from tourism to the digitization of human behavior. | ||||
| Address | Verliko Tarmovo; Bulgaria; September 2015 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | DiPP | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @DiR2016 | Serial | 2792 | ||
| Permanent link to this record | |||||
| Author | Aniol Lidon; Xavier Giro; Marc Bolaños; Petia Radeva; Markus Seidl; Matthias Zeppelzauer | ||||
| Title | UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images | Type | Conference Article | ||
| Year | 2015 | Publication | 2015 MediaEval Retrieving Diverse Images Task | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity. | ||||
| Address | Wurzen; Germany; September 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | MediaEval | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @LGB2016 | Serial | 2793 | ||
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| Author | Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci | ||||
| Title | Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting | Type | Book Whole | ||
| Year | 2016 | Publication | Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting | Abbreviated Journal | |
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 9780128110188 | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ BZZ2016 | Serial | 2821 | ||
| Permanent link to this record | |||||
| Author | Petia Radeva | ||||
| Title | Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? | Type | Conference Article | ||
| Year | 2016 | Publication | 19th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | ||
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| Address | Barcelona; October 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CCIA | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ Rad2016 | Serial | 2832 | ||
| Permanent link to this record | |||||
| Author | Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva | ||||
| Title | With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | |||||
| Abstract | Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPR | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ ADR2016d | Serial | 2835 | ||
| Permanent link to this record | |||||
| Author | Pedro Herruzo; Marc Bolaños; Petia Radeva | ||||
| Title | Can a CNN Recognize Catalan Diet? | Type | Book Chapter | ||
| Year | 2016 | Publication | AIP Conference Proceedings | Abbreviated Journal | |
| Volume | 1773 | Issue | Pages | ||
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| Abstract | CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes. |
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Area | Expedition | Conference | |||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ HBR2016 | Serial | 2837 | ||
| Permanent link to this record | |||||
| Author | Laura Igual; Santiago Segui | ||||
| Title | Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science | Type | Book Whole | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-215 | ||
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| Publisher | 978-3-319-50016-4 | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-319-50016-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ IgS2017 | Serial | 3027 | ||
| Permanent link to this record | |||||
| Author | Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez | ||||
| Title | DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Abbreviated Journal | JSTAEOR |
| Volume | 13 | Issue | Pages | 726-737 | |
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| Abstract | One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. | ||||
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| Area | Expedition | Conference | |||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ TRR2020 | Serial | 3410 | ||
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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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| 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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| Area | Expedition | Conference | BIODEVICES | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ ANK2020 | Serial | 3526 | ||
| Permanent link to this record | |||||
| Author | Petia Radeva | ||||
| Title | Uncertainty Modeling within an End-to-end Framework for Food Image Analysis | Type | Conference Article | ||
| Year | 2020 | Publication | 1st DELTA | Abbreviated Journal | |
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| Area | Expedition | Conference | DELTA | ||
| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ Rad2020 | Serial | 3527 | ||
| Permanent link to this record | |||||
| Author | Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck; Andreas Nürnberger | ||||
| Title | Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | CoRR abs/2006.02570
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoniæ and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model. |
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| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ CSS2020 | Serial | 3534 | ||
| Permanent link to this record | |||||
| Author | Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva | ||||
| Title | Eating Habits Discovery in Egocentric Photo-streams | Type | Miscellaneous | ||
| Year | 2020 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | CoRR abs/2009.07646
Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioural pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals. |
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| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ TGM2020 | Serial | 3536 | ||
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| Author | Giovanni Maria Farinella; Petia Radeva; Jose Braz | ||||
| Title | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications | Type | Book Whole | ||
| Year | 2020 | Publication | Proceedings of the 15th International Joint Conference on Computer Vision; Imaging and Computer Graphics Theory and Applications; VISIGRAPP 2020 | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | ||
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| Notes |
MILAB | Approved | no | ||
| Call Number | Admin @ si @ FRB2020a | Serial | 3546 | ||
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