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Author (down) F. Javier Sanchez; Jordi Vitria; Enric Marti edit  openurl
  Title Transformaciones Morfológicas de Polígonos Isotéticos Type Conference Article
  Year 1991 Publication Primer Congreso Español de Informática Gráfica. Abbreviated Journal  
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  Area Expedition Conference  
  Notes OR;IAM;MV Approved no  
  Call Number IAM @ iam @ SVM1991 Serial 1648  
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Author (down) F. Javier Sanchez; Jordi Vitria edit  isbn
openurl 
  Title ViLi + : Extended Lisp for image Processing and Computer Vision. Type Conference Article
  Year 1994 Publication Progress in Image Analysis and Processing III Abbreviated Journal  
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  Corporate Author Thesis  
  Publisher World Scientific Place of Publication Editor S.Impedovo  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 981-02-1552-5 Medium  
  Area Expedition Conference  
  Notes MV;OR Approved no  
  Call Number BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 Serial 114  
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Author (down) F. de la Torre; Jordi Vitria; Petia Radeva; J. Melenchon edit  openurl
  Title EigenFiltering for flexible Eigentracking. Type Conference Article
  Year 2000 Publication 15 th International Conference on Pattern Recognition Abbreviated Journal  
  Volume 3 Issue Pages 1118-1121  
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  Address Barcelona.  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes OR;MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ TVR2000 Serial 179  
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Author (down) Eva Costa edit  openurl
  Title Localitzacio i seguiment de persones amb una camera amb Pan, Tilt i Zoom Type Report
  Year 2001 Publication CVC Technical Report #51 Abbreviated Journal  
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  Address CVC (UAB)  
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  Notes Approved no  
  Call Number Admin @ si @ Cos2001 Serial 87  
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Author (down) Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title Comparison of two non-linear model-based control strategies for autonomous vehicles Type Conference Article
  Year 2016 Publication 24th Mediterranean Conference on Control and Automation Abbreviated Journal  
  Volume Issue Pages 846-851  
  Keywords Autonomous Driving; Control  
  Abstract This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.  
  Address Athens; Greece; June 2016  
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  Area Expedition Conference MED  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number ADAS @ adas @ ASP2016 Serial 2750  
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Author (down) Esteve Cervantes; Long Long Yu; Andrew Bagdanov; Marc Masana; Joost Van de Weijer edit   pdf
openurl 
  Title Hierarchical Part Detection with Deep Neural Networks Type Conference Article
  Year 2016 Publication 23rd IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages  
  Keywords Object Recognition; Part Detection; Convolutional Neural Networks  
  Abstract Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection. In a single network we first detect the object and then regress to part location proposals based only on the feature representation inside the object. Experiments show that our hierarchical approach outperforms a network which directly regresses the part locations. We also show that our approach obtains part detection accuracy comparable or better than state-of-the-art on the CUB-200 bird and Fashionista clothing item datasets with only a fraction of the number of part proposals.  
  Address Phoenix; Arizona; USA; September 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ICIP  
  Notes LAMP; 600.106 Approved no  
  Call Number Admin @ si @ CLB2016 Serial 2762  
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Author (down) Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya edit  openurl
  Title Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” Type Journal
  Year 2018 Publication Informaciones Psiquiatricas Abbreviated Journal  
  Volume 232 Issue Pages 47-59  
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  ISSN 0210-7279 ISBN Medium  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ FAA2018 Serial 3214  
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Author (down) Estefania Talavera; Petia Radeva; Nicolai Petkov edit  url
openurl 
  Title Towards Emotion Retrieval in Egocentric PhotoStream Type Miscellaneous
  Year 2019 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract CoRR abs/1905.04107
The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer's days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera's wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams.
 
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ TRP2019 Serial 3381  
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Author (down) Estefania Talavera; Nicolai Petkov; Petia Radeva edit   pdf
url  doi
openurl 
  Title Unsupervised Routine Discovery in Egocentric Photo-Streams Type Conference Article
  Year 2019 Publication 18th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 11678 Issue Pages 576-588  
  Keywords Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis  
  Abstract The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.  
  Address Salermo; Italy; September 2019  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ TPR2019a Serial 3367  
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Author (down) Estefania Talavera; Nicolai Petkov; Petia Radeva edit  url
openurl 
  Title Towards Unsupervised Familiar Scene Recognition in Egocentric Videos Type Miscellaneous
  Year 2019 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
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  Abstract CoRR abs/1905.04093
Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip.
 
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  Notes MILAB; no menciona Approved no  
  Call Number Admin @ si @ TPR2019b Serial 3379  
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Author (down) Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva edit  doi
isbn  openurl
  Title R-clustering for egocentric video segmentation Type Conference Article
  Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal  
  Volume 9117 Issue Pages 327-336  
  Keywords Temporal video segmentation; Egocentric videos; Clustering  
  Abstract In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods.  
  Address Santiago de Compostela; España; June 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-19389-2 Medium  
  Area Expedition Conference IbPRIA  
  Notes MILAB Approved no  
  Call Number Admin @ si @ TDB2015 Serial 2597  
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Author (down) Estefania Talavera; Maria Leyva-Vallina; Md. Mostafa Kamal Sarker; Domenec Puig; Nicolai Petkov; Petia Radeva edit   pdf
url  openurl
  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.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ TLM2020 Serial 3380  
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Author (down) Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva edit  url
doi  openurl
  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.  
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ TWP2020 Serial 3435  
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Author (down) Estefania Talavera; Andreea Glavan; Alina Matei; Petia Radeva edit   pdf
openurl 
  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.
 
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  Notes MILAB Approved no  
  Call Number Admin @ si @ TGM2020 Serial 3536  
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Author (down) Estefania Talavera; Alexandre Cola; Nicolai Petkov; Petia Radeva edit   pdf
url  doi
openurl 
  Title Towards Egocentric Person Re-identification and Social Pattern Analysis. Type Book Chapter
  Year 2019 Publication Frontiers in Artificial Intelligence and Applications Abbreviated Journal  
  Volume 310 Issue Pages 203 - 211  
  Keywords  
  Abstract CoRR abs/1905.04073
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.
 
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  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ TCP2019 Serial 3377  
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