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Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
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Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
10255 |
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Pages |
287-294 |
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Keywords |
Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
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Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
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Faro; Portugal; June 2017 |
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L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
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978-3-319-58837-7 |
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IbPRIA |
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Notes |
DAG; 602.006; 600.097; 600.121 |
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Call Number |
Admin @ si @ RFV2017 |
Serial |
2952 |
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Author |
Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Title |
Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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Book Chapter |
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Year |
2017 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Call Number |
Admin @ si @ JLR2017 |
Serial |
2928 |
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Author |
Pau Riba; Alicia Fornes; Josep Llados |
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Title |
Towards the Alignment of Handwritten Music Scores |
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Book Chapter |
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Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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Volume |
9657 |
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103-116 |
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Keywords |
Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment |
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Abstract |
It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
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Bart Lamiroy; R Dueire Lins |
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978-3-319-52158-9 |
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DAG; 600.097; 602.006; 600.121 |
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Admin @ si @ RFL2017 |
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2955 |
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Author |
Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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Year |
2017 |
Publication |
6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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10550 |
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Pages |
151-159 |
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Abstract |
Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Quebec; Canada; September 2017 |
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CLIP |
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Notes |
IAM; 600.096; 600.075; 600.145 |
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Call Number |
Admin @ si @ GRM2017 |
Serial |
2957 |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study |
Type |
Conference Article |
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Year |
2017 |
Publication |
International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) |
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An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identi cation of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
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Quebec; Canada; September 2017 |
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MICCAIW |
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MILAB; no proj |
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no |
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Call Number |
Admin @ si @ BCR2017 |
Serial |
2968 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Top-Down Deep Appearance Attention for Action Recognition |
Type |
Conference Article |
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Year |
2017 |
Publication |
20th Scandinavian Conference on Image Analysis |
Abbreviated Journal |
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Volume |
10269 |
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Pages |
297-309 |
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Keywords |
Action recognition; CNNs; Feature fusion |
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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. |
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Tromso; June 2017 |
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SCIA |
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Notes |
LAMP; 600.109; 600.068; 600.120 |
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Call Number |
Admin @ si @ RKW2017b |
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3039 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Title |
Ontology-Based Understanding of Architectural Drawings |
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Book Chapter |
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Year |
2017 |
Publication |
International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
9657 |
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75-85 |
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Keywords |
Graphics recognition; Floor plan analysi; Domain ontology |
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Abstract |
In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
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DAG; 600.121 |
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Call Number |
Admin @ si @ HRL2017 |
Serial |
3086 |
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