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Fadi Dornaika, & Angel Sappa. (2006). 3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm. In 21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541.
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Fadi Dornaika, & Angel Sappa. (2006). Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models. In 8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684.
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Fadi Dornaika, & Angel Sappa. (2006). 3D Motion from Image Derivatives using the Least Trimmed Square Regression. In International Workshop on Intelligent Computing in Pattern Analysis/Synthesis (IWICPAS´06), LNCS 4153: 76–84.
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Fadi Dornaika, & Angel Sappa. (2007). SFM for Planar Scenes: a Direct and Robust Approach. In book chapter: Informatics in Control, Automation and Robotics II, Ed. J. Filipe, J. Ferrier, J. Cetto and M. Carvalho, pp. 129–136. (best papers ICINCO 2005).
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Fadi Dornaika, & Angel Sappa. (2008). Real Time Image Registration for Planar Structure and 3D Sensor Pose Estimation. In Asim Bhatti (Ed.), Stereo Vision (Vol. 18, 299–316).
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F.Guirado, Ana Ripoll, C.Roig, Aura Hernandez-Sabate, & Emilio Luque. (2006). Exploiting Throughput for Pipeline Execution in Streaming Image Processing Applications. In UAB, E. N. W, & et al. (Eds.), Euro-Par 2006 Parallel Processing (Vol. 4128, pp. 1095–1105). Lecture Notes In Computer Science. Dresden, Germany (European Union): Springer-Verlag Berlin Heidelberg.
Abstract: There is a large range of image processing applications that act on an input sequence of image frames that are continuously received. Throughput is a key performance measure to be optimized when execu- ting them. In this paper we propose a new task replication methodology for optimizing throughput for an image processing application in the field of medicine. The results show that by applying the proposed methodo- logy we are able to achieve the desired throughput in all cases, in such a way that the input frames can be processed at any given rate.
Keywords: 12th International Euro–Par Conference
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Estefania Talavera, Alexandre Cola, Nicolai Petkov, & Petia Radeva. (2019). Towards Egocentric Person Re-identification and Social Pattern Analysis. In Frontiers in Artificial Intelligence and Applications (Vol. 310, pp. 203–211).
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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Ernest Valveny, Salvatore Tabbone, & Oriol Ramos Terrades. (2008). Performance Characterization of Shape Descriptors for Symbol Representation. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 278–287). LNCS.
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Ernest Valveny, Philippe Dosch, & Alicia Fornes. (2008). Report on the Third Contest on Symbol Recognition. In J.M. Ogier J. L. W. Liu (Ed.), Graphics Recognition: Recent Advances and New Opportunities (Vol. 5046, 321–328). LNCS.
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Ernest Valveny, & Philippe Dosch. (2004). Performance Evaluation of Symbol Recognition. In A. D.(E.) S. Marinai (Ed.), Document Analysis Systems (Vol. 3163, 354–365).
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Ernest Valveny, Oriol Ramos Terrades, Joan Mas, & Marçal Rusiñol. (2013). Interactive Document Retrieval and Classification. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 17–30). Springer Berlin Heidelberg.
Abstract: In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
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Enric Marti, Jordi Regincos, Juan Jose Villanueva, & Jaime Lopez-Krahe. (1994). Line drawing interpretation as polyhedral objects to man-machine interaction in CAD systems. In Advances in Pattern Recognition and Image Analysis, (pp. 158–169). World Scientific Pub.
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Eduard Vazquez, Ramon Baldrich, Javier Vazquez, & Maria Vanrell. (2007). Topological histogram reduction towards colour segmentation. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:55–62.
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Eduard Vazquez, Francesc Tous, Ramon Baldrich, & Maria Vanrell. (2006). n-Dimensional Distribution Reduction Preserving its Structure. In Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175.
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E. Tavalera, Mariella Dimiccoli, Marc Bolaños, Maedeh Aghaei, & Petia Radeva. (2015). Regularized Clustering for Egocentric Video Segmentation. In Pattern Recognition and Image Analysis (pp. 327–336). LNCS. Springer International Publishing.
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 energyminimization 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 techniques in an energy-minimization framework that serves disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames 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.
Keywords: Temporal video segmentation ; Egocentric videos ; Clustering
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