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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
	Volume		Issue		Pages
	Keywords
	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
	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	BIODEVICES
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ANK2020			Serial	3526
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	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
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	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	DELTA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ Rad2020			Serial	3527
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	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
	Keywords
	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.
	Address
	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
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ CSS2020			Serial	3534
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	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
	Keywords
	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.
	Address
	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
	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
	Keywords
	Abstract
	Address
	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
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ FRB2020a			Serial	3546
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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	5	Issue		Pages
	Keywords
	Abstract
	Address
	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
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ FRB2020b			Serial	3547
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	Author	Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva
	Title	Hierarchical approach to classify food scenes in egocentric photo-streams			Type	Journal Article
	Year	2020	Publication	IEEE Journal of Biomedical and Health Informatics	Abbreviated Journal	J-BHI
	Volume	24	Issue	3	Pages	866 - 877
	Keywords
	Abstract	Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56\% and 65\%, respectively, clearly outperforming the baseline methods.
	Address
	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
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TLM2020			Serial	3380
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	Author	Khalid El Asnaoui; Petia Radeva
	Title	Automatically Assess Day Similarity Using Visual Lifelogs			Type	Journal Article
	Year	2020	Publication	International Journal of Intelligent Systems	Abbreviated Journal	IJIS
	Volume	29	Issue		Pages	298–310
	Keywords
	Abstract	Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the lifelogging images of a person. We propose a technique to measure the similarity between images based on the Swain’s distance and generalize it to detect the similarity between daily visual data. To this purpose, we apply the dynamic time warping (DTW) combined with the Swain’s distance for final day similarity estimation. For validation, we apply our technique on the Egocentric Dataset of University of Barcelona (EDUB) of 4912 daily images acquired by four persons with preliminary encouraging results.
	Address
	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
	Notes	MILAB; no proj			Approved	no
	Call Number	AsR2020			Serial	3409
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	Author	Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva
	Title	Topic modelling for routine discovery from egocentric photo-streams			Type	Journal Article
	Year	2020	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	104	Issue		Pages	107330
	Keywords	Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling
	Abstract	Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.
	Address
	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
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TWP2020			Serial	3435
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	Author	Alejandro Cartas; Petia Radeva; Mariella Dimiccoli
	Title	Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications			Type	Journal Article
	Year	2020	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	8	Issue		Pages	77344 - 77363
	Keywords
	Abstract	Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications.
	Address
	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
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ CRD2020			Serial	3436
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	Author	Eduardo Aguilar; Petia Radeva
	Title	Uncertainty-aware integration of local and flat classifiers for food recognition			Type	Journal Article
	Year	2020	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	136	Issue		Pages	237-243
	Keywords
	Abstract	Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers local classifiers, in addition to a flat classifier. In order to make a decision about which approach to use, we define different criteria that take into account both the analysis of the Epistemic Uncertainty estimated from the ‘children’ classifiers and the prediction from the ‘parent’ classifier. We evaluate our proposal using three Uncertainty estimation methods, tested on two public food datasets. The results show that the proposed method reduces parent-child error propagation in hierarchical schemes and improves classification results compared to the single flat classifier, meanwhile maintains good performance regardless the Uncertainty estimation method chosen.
	Address
	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
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2020			Serial	3525
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	Author	Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva
	Title	Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams			Type	Conference Article
	Year	2020	Publication	ECCV Workshops	Abbreviated Journal
	Volume	12538	Issue		Pages	469-484
	Keywords
	Abstract	The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.
	Address	Virtual; August 2020
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECCVW
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ MTM2020			Serial	3528
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	Author	Cristhian A. Aguilera-Carrasco; Cristhian Aguilera; Cristobal A. Navarro; Angel Sappa
	Title	Fast CNN Stereo Depth Estimation through Embedded GPU Devices			Type	Journal Article
	Year	2020	Publication	Sensors	Abbreviated Journal	SENS
	Volume	20	Issue	11	Pages	3249
	Keywords	stereo matching; deep learning; embedded GPU
	Abstract	Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 × 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices.
	Address
	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
	Notes	MSIAU; 600.122			Approved	no
	Call Number	Admin @ si @ AAN2020			Serial	3428
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	Author	Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski
	Title	Kornia: an Open Source Differentiable Computer Vision Library for PyTorch			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aspen; Colorado; USA; March 2020
	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	WACV
	Notes	MSIAU; 600.122; 600.130			Approved	no
	Call Number	Admin @ si @ RMP2020			Serial	3291
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	Author	Henry Velesaca; Raul Mira; Patricia Suarez; Christian X. Larrea; Angel Sappa
	Title	Deep Learning Based Corn Kernel Classification			Type	Conference Article
	Year	2020	Publication	1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learningbased approach, the Mask R-CNN architecture, while the classification is performed hrough a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches are provided showing improvements with the proposed pipeline.
	Address	Virtual CVPR
	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	CVPRW
	Notes	MSIAU; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ VMS2020			Serial	3430
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