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Author Miquel Ferrer; Ernest Valveny; F. Serratosa edit  openurl
  Title Spectral Median Graphs Applied to Graphical Symbol Recognition Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), J.P. Martinez–Trinidad et al. (Eds.), LNCS 4225: 774–783 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address (up) Cancun (Mexico)  
  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 DAG Approved no  
  Call Number DAG @ dag @ FVS2006b Serial 698  
Permanent link to this record
 

 
Author Karla Lizbeth Caballero; Joel Barajas; Oriol Pujol; Neus Salvatella; Petia Radeva edit  openurl
  Title In-Vivo IVUS Tissue Classification: A Comparison Between RF Signal Analysis and Reconstructed Images Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 137–146 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address (up) Cancun (Mexico)  
  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;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ CBP2006c Serial 724  
Permanent link to this record
 

 
Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva edit   pdf
doi  openurl
  Title Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 4225 Issue Pages 178–187  
  Keywords  
  Abstract This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.  
  Address (up) Cancun (Mexico)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin Heidelberg Editor .F. Mart ́ınez-Trinidad et al  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f Serial 728  
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Author Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva edit   pdf
doi  openurl
  Title A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment Type Book Chapter
  Year 2006 Publication 11th Iberoamerican Congress on Pattern Recognition Abbreviated Journal  
  Volume 4225 Issue Pages 188–197  
  Keywords  
  Abstract Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.  
  Address (up) Cancun (Mexico)  
  Corporate Author Thesis  
  Publisher Springer Verlag Place of Publication Berlin-Heidelberg Editor J.P. Martinez–Trinidad et al  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area 800 Expedition Conference CIARP06  
  Notes MV;OR;MILAB;SIAI Approved no  
  Call Number BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e Serial 729  
Permanent link to this record
 

 
Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit   pdf
openurl 
  Title With whom do I interact with? Social interaction detection in egocentric photo-streams Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.  
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes MILAB Approved no  
  Call Number Admin @ si @ADR2016a Serial 2791  
Permanent link to this record
 

 
Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov edit   pdf
openurl 
  Title Improving Text Proposals for Scene Images with Fully Convolutional Networks Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Text Proposals have emerged as a class-dependent version of object proposals – efficient approaches to reduce the search space of possible text object locations in an image. Combined with strong word classifiers, text proposals currently yield top state of the art results in end-to-end scene text
recognition. In this paper we propose an improvement over the original Text Proposals algorithm of [1], combining it with Fully Convolutional Networks to improve the ranking of proposals. Results on the ICDAR RRC and the COCO-text datasets show superior performance over current state-of-the-art.
 
  Address (up) Cancun; Mexico; December 2016  
  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 ICPRW  
  Notes DAG; LAMP; 600.084 Approved no  
  Call Number Admin @ si @ BGN2016 Serial 2823  
Permanent link to this record
 

 
Author Hugo Jair Escalante; Victor Ponce; Jun Wan; Michael A. Riegler; Baiyu Chen; Albert Clapes; Sergio Escalera; Isabelle Guyon; Xavier Baro; Pal Halvorsen; Henning Muller; Martha Larson edit   pdf
url  doi
openurl 
  Title ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An Overview Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from
short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost
200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to
allow researchers to keep making progress in the four tracks.
 
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes HuPBA; 602.143;MV Approved no  
  Call Number Admin @ si @ EPW2016 Serial 2827  
Permanent link to this record
 

 
Author Marc Bolaños; Petia Radeva edit   pdf
url  doi
openurl 
  Title Simultaneous Food Localization and Recognition Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract CoRR abs/1604.07953
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays – object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images.
 
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes MILAB; no proj Approved no  
  Call Number Admin @ si @ BoR2016 Serial 2834  
Permanent link to this record
 

 
Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva edit   pdf
url  doi
openurl 
  Title With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.  
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes MILAB Approved no  
  Call Number Admin @ si @ ADR2016d Serial 2835  
Permanent link to this record
 

 
Author Fatemeh Noroozi; Marina Marjanovic; Angelina Njegus; Sergio Escalera; Gholamreza Anbarjafari edit  openurl
  Title Fusion of Classifier Predictions for Audio-Visual Emotion Recognition Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are
computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence
outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE’05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches.
 
  Address (up) Cancun; Mexico; December 2016  
  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 ICPRW  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ NMN2016 Serial 2839  
Permanent link to this record
 

 
Author Anjan Dutta; Umapada Pal; Josep Llados edit  url
openurl 
  Title Compact Correlated Features for Writer Independent Signature Verification Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.  
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ DPL2016 Serial 2875  
Permanent link to this record
 

 
Author Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari edit  doi
openurl 
  Title Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume 10165 Issue Pages  
  Keywords  
  Abstract Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.  
  Address (up) Cancun; Mexico; December 2016  
  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 ICPR  
  Notes HuPBA; ISE; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ BHR2016 Serial 2902  
Permanent link to this record
 

 
Author Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title Human Head Pose Estimation on SASE database using Random Hough Regression Forests Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Workshops Abbreviated Journal  
  Volume 10165 Issue Pages  
  Keywords  
  Abstract In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.  
  Address (up) Cancun; Mexico; December 2016  
  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 ICPRW  
  Notes HuPBA; Approved no  
  Call Number Admin @ si @ LEA2016b Serial 2910  
Permanent link to this record
 

 
Author M. Bressan; Jordi Vitria edit  openurl
  Title Independent Modes of Variation in Point Distribution Models Type Miscellaneous
  Year 2001 Publication In C. Arcelli, L.P. Cordella, G. Sanniti di Baja (Eds.): Visual Form 2001 4tth International Workshop on Visual Visual Form 2001 4tth International Workshop on Visual Form, IWVF4, Proceedings, LNCS 2059, Springer Verlag, 123 Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address (up) Capri, Italia  
  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 OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ BVi2001 Serial 80  
Permanent link to this record
 

 
Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu edit   pdf
url  doi
isbn  openurl
  Title Synthetic ground truth dataset to detect shadow cast by static objects in outdoor Type Conference Article
  Year 2012 Publication 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications Abbreviated Journal  
  Volume Issue Pages art. 11  
  Keywords  
  Abstract In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software.  
  Address (up) Capri, Italy  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4503-1405-3 Medium  
  Area Expedition Conference VIGTA  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ ISR2012a Serial 2037  
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