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Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Human Activity Recognition from Accelerometer Data using a Wearable Device Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages (up) 289-296
Keywords
Abstract Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium
Area Expedition Conference IbPRIA
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011a Serial 1735
Permanent link to this record
 
 
Author Albert Gordo; Ernest Valveny
Title The diagonal split: A pre-segmentation step for page layout analysis & classification Type Conference Article
Year 2009 Publication 4th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 5524 Issue Pages (up) 290–297
Keywords
Abstract Document classification is an important task in all the processes related to document storage and retrieval. In the case of complex documents, structural features are needed to achieve a correct classification. Unfortunately, physical layout analysis is error prone. In this paper we present a pre-segmentation step based on a divide & conquer strategy that can be used to improve the page segmentation results, independently of the segmentation algorithm used. This pre-segmentation step is evaluated in classification and retrieval using the selective CRLA algorithm for layout segmentation together with a clustering based on the voronoi area diagram, and tested on two different databases, MARG and Girona Archives.
Address Póvoa de Varzim, Portugal
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 978-3-642-02171-8 Medium
Area Expedition Conference IbPRIA
Notes DAG Approved no
Call Number DAG @ dag @ Gov2009b Serial 1176
Permanent link to this record
 
 
Author Wenjuan Gong; Andrew Bagdanov; Xavier Roca; Jordi Gonzalez
Title Automatic Key Pose Selection for 3D Human Action Recognition Type Conference Article
Year 2010 Publication 6th International Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 6169 Issue Pages (up) 290–299
Keywords
Abstract This article describes a novel approach to the modeling of human actions in 3D. The method we propose is based on a “bag of poses” model that represents human actions as histograms of key-pose occurrences over the course of a video sequence. Actions are first represented as 3D poses using a sequence of 36 direction cosines corresponding to the angles 12 joints form with the world coordinate frame in an articulated human body model. These pose representations are then projected to three-dimensional, action-specific principal eigenspaces which we refer to as aSpaces. We introduce a method for key-pose selection based on a local-motion energy optimization criterion and we show that this method is more stable and more resistant to noisy data than other key-poses selection criteria for action recognition.
Address
Corporate Author Thesis
Publisher Springer Verlag 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 978-3-642-14060-0 Medium
Area Expedition Conference AMDO
Notes ISE Approved no
Call Number DAG @ dag @ GBR2010 Serial 1317
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Author Fernando Vilariño; Gerard Lacey; Jiang Zhou; Hugh Mulcahy; Stephen Patchett
Title Automatic Labeling of Colonoscopy Video for Cancer Detection Type Conference Article
Year 2007 Publication In Proc. berian Conference, IbPRIA Abbreviated Journal
Volume Issue Pages (up) 290-297
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 800 Expedition Conference
Notes MV;SIAI Approved no
Call Number fernando @ fernando @ Serial 2431
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Author Esmitt Ramirez; Carles Sanchez; Debora Gil
Title Localizing Pulmonary Lesions Using Fuzzy Deep Learning Type Conference Article
Year 2019 Publication 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages (up) 290-294
Keywords
Abstract The usage of medical images is part of the clinical daily in several healthcare centers around the world. Particularly, Computer Tomography (CT) images are an important key in the early detection of suspicious lung lesions. The CT image exploration allows the detection of lung lesions before any invasive procedure (e.g. bronchoscopy, biopsy). The effective localization of lesions is performed using different image processing and computer vision techniques. Lately, the usage of deep learning models into medical imaging from detection to prediction shown that is a powerful tool for Computer-aided software. In this paper, we present an approach to localize pulmonary lung lesion using fuzzy deep learning. Our approach uses a simple convolutional neural network based using the LIDC-IDRI dataset. Each image is divided into patches associated a probability vector (fuzzy) according their belonging to anatomical structures on a CT. We showcase our approach as part of a full CAD system to exploration, planning, guiding and detection of pulmonary lesions.
Address Timisoara; Rumania; September 2019
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 SYNASC
Notes IAM; 600.145; 600.140; 601.337; 601.323 Approved no
Call Number Admin @ si @ RSG2019 Serial 3531
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Author Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
Title Comparing Combinations of Feature Regions for Panoramic VSLAM Type Conference Article
Year 2007 Publication 4th International Conference on Informatics in Control, Automation and Robotics Abbreviated Journal
Volume Issue Pages (up) 292–297
Keywords
Abstract
Address Angers (France)
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 ICINCO
Notes RV;ADAS Approved no
Call Number Admin @ si @ RLA2007 Serial 900
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Author Laura Igual; Santiago Segui; Jordi Vitria; Fernando Azpiroz; Petia Radeva
Title Eigenmotion-Based Detection of Intestinal Contractions Type Conference Article
Year 2007 Publication Computer Analysis of Images and Patterns, 12th International Conference Abbreviated Journal
Volume 4673 Issue Pages (up) 293–300
Keywords
Abstract
Address Vienna (Austria)
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 978-3-540-74271-5 Medium
Area Expedition Conference CAIP
Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ ISV2007a Serial 895
Permanent link to this record
 
 
Author David Fernandez; Josep Llados; Alicia Fornes
Title A graph-based approach for segmenting touching lines in historical handwritten documents Type Journal Article
Year 2014 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 17 Issue 3 Pages (up) 293-312
Keywords Text line segmentation; Handwritten documents; Document image processing; Historical document analysis
Abstract Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-2833 ISBN Medium
Area Expedition Conference
Notes DAG; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FLF2014 Serial 2459
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Author David Aldavert; Marçal Rusiñol
Title Manuscript text line detection and segmentation using second-order derivatives analysis Type Conference Article
Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages (up) 293 - 298
Keywords text line detection; text line segmentation; text region detection; second-order derivatives
Abstract In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
Address Viena; Austria; April 2018
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 DAS
Notes DAG; 600.084; 600.129; 302.065; 600.121 Approved no
Call Number Admin @ si @ AlR2018a Serial 3104
Permanent link to this record
 
 
Author Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone
Title Towards Modelling an Attention-Based Text Localization Process Type Conference Article
Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 7887 Issue Pages (up) 296-303
Keywords text localization; visual attention; eye guidance
Abstract This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented.
Address Madeira; Portugal; June 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg 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 978-3-642-38627-5 Medium
Area Expedition Conference IbPRIA
Notes DAG Approved no
Call Number Admin @ si @ CKL2013 Serial 2291
Permanent link to this record
 
 
Author Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta
Title Diaphragm border detection in coronary X-ray angiographies: New method and applications Type Journal Article
Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG
Volume 38 Issue 4 Pages (up) 296-305
Keywords
Abstract X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.
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; LAMP; 600.079 Approved no
Call Number Admin @ si @ PCR2014 Serial 2468
Permanent link to this record
 
 
Author O. Fors; A. Richichi; Xavier Otazu; J. Nuñez
Title A new wavelet-based approach for the automated treatment of large sets of lunar occultation data Type Journal
Year 2008 Publication Astronomy and Astrohysics Abbreviated Journal
Volume 480 Issue Pages (up) 297–304
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 CIC Approved no
Call Number CAT @ cat @ FRO2008 Serial 934
Permanent link to this record
 
 
Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title Recognition of Multi-oriented Touching Characters in Graphical Documents Type Conference Article
Year 2008 Publication Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, Abbreviated Journal
Volume 16 Issue Pages (up) 297–304
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 ICVGIP ’08
Notes DAG Approved no
Call Number DAG @ dag @ RPL2008c Serial 1080
Permanent link to this record
 
 
Author Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans
Title Improved RGB-D-T based Face Recognition Type Journal Article
Year 2016 Publication IET Biometrics Abbreviated Journal BIO
Volume 5 Issue 4 Pages (up) 297 - 303
Keywords
Abstract Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes.
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 HuPBA;MILAB; Approved no
Call Number Admin @ si @ OCN2016 Serial 2854
Permanent link to this record
 
 
Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen
Title Top-Down Deep Appearance Attention for Action Recognition Type Conference Article
Year 2017 Publication 20th Scandinavian Conference on Image Analysis Abbreviated Journal
Volume 10269 Issue Pages (up) 297-309
Keywords Action recognition; CNNs; Feature fusion
Abstract Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
Address Tromso; June 2017
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 SCIA
Notes LAMP; 600.109; 600.068; 600.120 Approved no
Call Number Admin @ si @ RKW2017b Serial 3039
Permanent link to this record