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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
User Verification From Walking Activity. First Steps Towards a Personal Verification System |
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Conference Article |
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2011 |
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1st International Conference on Pervasive and Embedded Computing and Communication Systems |
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Algarve, Portugal |
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PECCS |
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MILAB;HuPBA |
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no |
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Admin @ si @ CPR2011c |
Serial |
1762 |
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Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
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Title |
Modeling long-term interactions to enhance action recognition |
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Conference Article |
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Year |
2021 |
Publication |
25th International Conference on Pattern Recognition |
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10351-10358 |
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In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
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January 2021 |
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ICPR |
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MILAB; |
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no |
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Admin @ si @ CRD2021 |
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3626 |
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Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
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Title |
User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis |
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Conference Article |
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2012 |
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7th Conference on Articulated Motion and Deformable Objects |
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7378 |
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1-11 |
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We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
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Mallorca |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-31566-4 |
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AMDO |
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HUPBA;MILAB |
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no |
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Admin @ si @ CRE2012 |
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2010 |
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Author |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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Year |
2018 |
Publication |
16th International Conference on Frontiers in Handwriting Recognition |
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528-533 |
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Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
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no |
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Call Number |
Admin @ si @ CRF2018 |
Serial |
3169 |
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Author |
M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination |
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Conference Article |
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2015 |
Publication |
IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 |
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4169 - 4172 |
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This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. |
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Milan; Italy; July 2015 |
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IGARSS |
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Notes |
LAMP; 600.079;MILAB |
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no |
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Call Number |
Admin @ si @ CRG2015 |
Serial |
2724 |
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Author |
J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier |
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Title |
Improving Document Matching Performance by Local Descriptor Filtering |
Type |
Conference Article |
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Year |
2015 |
Publication |
6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1216 - 1220 |
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In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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Address |
Nancy; France; August 2015 |
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CBDAR |
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Notes |
DAG; 600.077; 601.223; 600.084 |
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no |
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Call Number |
Admin @ si @ CRO2015a |
Serial |
2680 |
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Author |
J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier; Josep Llados |
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Title |
A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
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621-625 |
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This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015 |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.084; 600.061; 601.223; 600.077 |
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no |
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Call Number |
Admin @ si @ CRO2015b |
Serial |
2685 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Document segmentation using relative location features |
Type |
Conference Article |
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Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
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1562-1565 |
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In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works. |
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Tsukuba Science City, Japan |
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ICPR |
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DAG |
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no |
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Admin @ si @ CrR2012 |
Serial |
2051 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
EM-Based Layout Analysis Method for Structured Documents |
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Conference Article |
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2014 |
Publication |
22nd International Conference on Pattern Recognition |
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315-320 |
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In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according
to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task. |
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1051-4651 |
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ICPR |
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Notes |
DAG; 602.006; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ CrR2014 |
Serial |
2530 |
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Author |
Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa |
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Title |
Fine-tuning based deep convolutional networks for lepidopterous genus recognition |
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Conference Article |
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2016 |
Publication |
21st Ibero American Congress on Pattern Recognition |
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467-475 |
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This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%. |
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Lima; Perú; November 2016 |
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CIARP |
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Notes |
ADAS; 600.086 |
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no |
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Call Number |
Admin @ si @ CRS2016 |
Serial |
2913 |
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Author |
H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent |
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Title |
Feature Selection Combining Genetic Algorithm and Adaboost Classifiers |
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Conference Article |
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2008 |
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19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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ICPR |
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DAG |
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no |
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Admin @ si @ CRT2008 |
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1872 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Handwritten Line Detection via an EM Algorithm |
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Conference Article |
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2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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718-722 |
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In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG |
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no |
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Call Number |
Admin @ si @ CrT2013 |
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2329 |
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Author |
Manuel Carbonell; Pau Riba; Mauricio Villegas; Alicia Fornes; Josep Llados |
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Title |
Named Entity Recognition and Relation Extraction with Graph Neural Networks in Semi Structured Documents |
Type |
Conference Article |
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2020 |
Publication |
25th International Conference on Pattern Recognition |
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The use of administrative documents to communicate and leave record of business information requires of methods
able to automatically extract and understand the content from
such documents in a robust and efficient way. In addition,
the semi-structured nature of these reports is specially suited
for the use of graph-based representations which are flexible
enough to adapt to the deformations from the different document
templates. Moreover, Graph Neural Networks provide the proper
methodology to learn relations among the data elements in
these documents. In this work we study the use of Graph
Neural Network architectures to tackle the problem of entity
recognition and relation extraction in semi-structured documents.
Our approach achieves state of the art results in the three
tasks involved in the process. Additionally, the experimentation
with two datasets of different nature demonstrates the good
generalization ability of our approach. |
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Virtual; January 2021 |
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ICPR |
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Notes |
DAG; 600.121 |
Approved |
no |
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Call Number |
Admin @ si @ CRV2020 |
Serial |
3509 |
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Author |
Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi |
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Title |
A Web-based Interactive Transcription Tool for Encrypted Manuscripts |
Type |
Conference Article |
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Year |
2020 |
Publication |
3rd International Conference on Historical Cryptology |
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52-59 |
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Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available. |
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Virtual; June 2020 |
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HistoCrypt |
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DAG; 600.140; 602.230; 600.121 |
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Admin @ si @ CSF2020 |
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3447 |
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Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi |
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Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images |
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2021 |
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4th International Conference on Historical Cryptology |
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34-37 |
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Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering. |
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Virtual; September 2021 |
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HistoCrypt |
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DAG; 602.230; 600.140; 600.121 |
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Admin @ si @ CSF2021 |
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3617 |
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| Permanent link to this record |
