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Author Marc Bolaños; Maite Garolera; Petia Radeva
Title Object Discovery using CNN Features in Egocentric Videos Type Conference Article
Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal
Volume 9117 Issue Pages 67-74
Keywords Object discovery; Egocentric videos; Lifelogging; CNN
Abstract Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach.
Address Santiago de Compostela; España; June 2015
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 0302-9743 ISBN (down) 978-3-319-19389-2 Medium
Area Expedition Conference IbPRIA
Notes MILAB Approved no
Call Number Admin @ si @ BGR2015 Serial 2596
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Author Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva
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 (down) 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 Onur Ferhat; Arcadi Llanza; Fernando Vilariño
Title A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios Type Conference Article
Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal
Volume 9117 Issue Pages 569-576
Keywords Eye tracking; Gaze estimation; Natural light; Webcam
Abstract We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system.
Address Santiago de Compostela; 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 (down) 978-3-319-19389-2 Medium
Area Expedition Conference IbPRIA
Notes MV;SIAI Approved no
Call Number Admin @ si @ FLV2015a Serial 2646
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Author Suman Ghosh; Ernest Valveny
Title A Sliding Window Framework for Word Spotting Based on Word Attributes Type Conference Article
Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal
Volume 9117 Issue Pages 652-661
Keywords Word spotting; Sliding window; Word attributes
Abstract In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.
Address Santiago de Compostela; 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 (down) 978-3-319-19389-2 Medium
Area Expedition Conference IbPRIA
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ GhV2015b Serial 2716
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Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta
Title Large-scale Graph Indexing using Binary Embeddings of Node Contexts Type Conference Article
Year 2015 Publication 10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition Abbreviated Journal
Volume 9069 Issue Pages 208-217
Keywords Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding
Abstract Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents.
Address Beijing; China; May 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN (down) 978-3-319-18223-0 Medium
Area Expedition Conference GbRPR
Notes DAG; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ RLF2015a Serial 2618
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Author Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen
Title Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging Type Conference Article
Year 2014 Publication 17th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 8896 Issue Pages 231-238
Keywords Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging
Abstract Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP.
Address Boston; USA; September 2014
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 (down) 978-3-319-14677-5 Medium
Area Expedition Conference STACOM
Notes IAM; ADAS; 600.060; 601.145; 600.076; 600.075 Approved no
Call Number Admin @ si @ MKF2014 Serial 2495
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Author Jorge Bernal; Debora Gil; Carles Sanchez; F. Javier Sanchez
Title Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis Type Conference Article
Year 2014 Publication 1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy Abbreviated Journal
Volume 8899 Issue Pages 1-10
Keywords Image Segmentation; Polyps, Colonoscopy; Valley Information; Energy Maps
Abstract In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
Address Boston; USA; September 2014
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 (down) 978-3-319-13409-3 Medium
Area Expedition Conference CARE
Notes MV; IAM; 600.044; 600.047; 600.060; 600.075 Approved no
Call Number Admin @ si @ BGS2014b Serial 2503
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Author Francesco Brughi; Debora Gil; Llorenç Badiella; Eva Jove Casabella; Oriol Ramos Terrades
Title Exploring the impact of inter-query variability on the performance of retrieval systems Type Conference Article
Year 2014 Publication 11th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 8814 Issue Pages 413–420
Keywords
Abstract This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.
Address Algarve; Portugal; October 2014
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 (down) 978-3-319-11757-7 Medium
Area Expedition Conference ICIAR
Notes IAM; DAG; 600.060; 600.061; 600.077; 600.075 Approved no
Call Number Admin @ si @ BGB2014 Serial 2559
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Author Oualid M. Benkarim; Petia Radeva; Laura Igual
Title Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
Year 2014 Publication 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 8563 Issue Pages 138-147
Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation
Abstract The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Address Palma de Mallorca; July 2014
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 (down) 978-3-319-08848-8 Medium
Area Expedition Conference AMDO
Notes MILAB; OR Approved no
Call Number Admin @ si @ BRI2014 Serial 2494
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu
Title Robust Head Gestures Recognition for Assistive Technology Type Book Chapter
Year 2014 Publication Pattern Recognition Abbreviated Journal
Volume 8495 Issue Pages 152-161
Keywords
Abstract This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.
Address
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 (down) 978-3-319-07490-0 Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ TSR2014b Serial 2505
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Author Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez
Title On-line lumen centre detection in gastrointestinal and respiratory endoscopy Type Conference Article
Year 2013 Publication Second International Workshop Clinical Image-Based Procedures Abbreviated Journal
Volume 8361 Issue Pages 31-38
Keywords Lumen centre detection; Bronchoscopy; Colonoscopy
Abstract We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %).
Address Nagoya; Japan; September 2013
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN (down) 978-3-319-05665-4 Medium
Area 800 Expedition Conference CLIP
Notes MV; IAM; 600.047; 600.044; 600.060 Approved no
Call Number Admin @ si @ SBG2013 Serial 2302
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Author Alex Pardo; Albert Clapes; Sergio Escalera; Oriol Pujol
Title Actions in Context: System for people with Dementia Type Conference Article
Year 2013 Publication 2nd International Workshop on Citizen Sensor Networks (Citisen2013) at the European Conference on Complex Systems Abbreviated Journal
Volume Issue Pages 3-14
Keywords Multi-modal data Fusion; Computer vision; Wearable sensors; Gesture recognition; Dementia
Abstract In the next forty years, the number of people living with dementia is expected to triple. In the last stages, people affected by this disease become dependent. This hinders the autonomy of the patient and has a huge social impact in time, money and effort. Given this scenario, we propose an ubiquitous system capable of recognizing daily specific actions. The system fuses and synchronizes data obtained from two complementary modalities – ambient and egocentric. The ambient approach consists in a fixed RGB-Depth camera for user and object recognition and user-object interaction, whereas the egocentric point of view is given by a personal area network (PAN) formed by a few wearable sensors and a smartphone, used for gesture recognition. The system processes multi-modal data in real-time, performing paralleled task recognition and modality synchronization, showing high performance recognizing subjects, objects, and interactions, showing its reliability to be applied in real case scenarios.
Address Barcelona; September 2013
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN (down) 978-3-319-04177-3 Medium
Area Expedition Conference ECCS
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ PCE2013 Serial 2354
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Author Fadi Dornaika; Alireza Bosaghzadeh; Bogdan Raducanu
Title Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition Type Conference Article
Year 2013 Publication Human Behavior Understanding 4th International Workshop Abbreviated Journal
Volume 8212 Issue Pages 124-135
Keywords
Abstract Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods.
Address Barcelona
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN (down) 978-3-319-02713-5 Medium
Area Expedition Conference HBU
Notes OR;MV Approved no
Call Number Admin @ si @ DBR2013 Serial 2315
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Author Andrea Gemelli; Sanket Biswas; Enrico Civitelli; Josep Llados; Simone Marinai
Title Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks Type Conference Article
Year 2022 Publication 17th European Conference on Computer Vision Workshops Abbreviated Journal
Volume 13804 Issue Pages 329–344
Keywords
Abstract Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection.
Address
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 (down) 978-3-031-25068-2 Medium
Area Expedition Conference ECCV-TiE
Notes DAG; 600.162; 600.140; 110.312 Approved no
Call Number Admin @ si @ GBC2022 Serial 3795
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Author Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds)
Title Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 Type Book Whole
Year 2022 Publication Frontiers in Handwriting Recognition. Abbreviated Journal
Volume 13639 Issue Pages
Keywords
Abstract
Address ICFHR 2022, Hyderabad, India, December 4–7, 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor Utkarsh Porwal; Alicia Fornes; Faisal Shafait
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN (down) 978-3-031-21648-0 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ PFS2022 Serial 3809
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