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Author | Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero | ||||
Title | e-Counterfeit: a mobile-server platform for document counterfeit detection | Type | Conference Article | ||
Year | 2017 | Publication | 14th IAPR International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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Abstract | This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms. | ||||
Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.061; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRL2018 | Serial | 3084 | ||
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Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez | ||||
Title | Procedural Generation of Videos to Train Deep Action Recognition Networks | Type | Conference Article | ||
Year | 2017 | Publication | 30th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 2594-2604 | ||
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Abstract | Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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Address | Honolulu; Hawaii; July 2017 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | ADAS; 600.076; 600.085; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SGC2017 | Serial | 3051 | ||
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Author | Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados | ||||
Title | ICDAR2017 Competition on Information Extraction in Historical Handwritten Records | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1389-1394 | ||
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Abstract | The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. | ||||
Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.225; 600.121 | Approved | no | ||
Call Number | Admin @ si @ FRB2017 | Serial | 3052 | ||
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Author | Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes; Sounak Dey | ||||
Title | Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 475-480 | ||
Keywords | document terms; information retrieval; affinity graph; graph of document terms; multiwriter; graph diffusion | ||||
Abstract | Information Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the
state-of-the-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RDL2017a | Serial | 3053 | ||
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Author | Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes | ||||
Title | Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 33-38 | ||
Keywords | graph embedding; hierarchical graph representation; graph clustering; stochastic graphlet embedding; graph classification | ||||
Abstract | Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).
Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support vector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DRL2017 | Serial | 3054 | ||
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Author | Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados | ||||
Title | Handwriting Recognition by Attribute embedding and Recurrent Neural Networks | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1038-1043 | ||
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Abstract | Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.225; 600.121 | Approved | no | ||
Call Number | Admin @ si @ TDF2017 | Serial | 3055 | ||
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Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
Title | Optical Music Recognition by Recurrent Neural Networks | Type | Conference Article | ||
Year | 2017 | Publication | 14th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 25-26 | ||
Keywords | Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory | ||||
Abstract | Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level | ||||
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ BRC2017 | Serial | 3056 | ||
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Author | Sounak Dey; Anjan Dutta; Josep Llados; Alicia Fornes; Umapada Pal | ||||
Title | Shallow Neural Network Model for Hand-drawn Symbol Recognition in Multi-Writer Scenario | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 31-32 | ||
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Abstract | One of the main challenges in hand drawn symbol recognition is the variability among symbols because of the different writer styles. In this paper, we present and discuss some results recognizing hand-drawn symbols with a shallow neural network. A neural network model inspired from the LeNet architecture has been used to achieve state-of-the-art results with
very less training data, which is very unlikely to the data hungry deep neural network. From the results, it has become evident that the neural network architectures can efficiently describe and recognize hand drawn symbols from different writers and can model the inter author aberration |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ DDL2017 | Serial | 3057 | ||
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Author | Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes | ||||
Title | Graph-based deep learning for graphics classification | Type | Conference Article | ||
Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 29-30 | ||
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Abstract | Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems |
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Area | Expedition | Conference | ICDAR | ||
Notes | DAG; 600.097; 601.302; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RDL2017b | Serial | 3058 | ||
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Author | Adria Rico; Alicia Fornes | ||||
Title | Camera-based Optical Music Recognition using a Convolutional Neural Network | Type | Conference Article | ||
Year | 2017 | Publication | 12th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 27-28 | ||
Keywords | optical music recognition; document analysis; convolutional neural network; deep learning | ||||
Abstract | Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results | ||||
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Area | Expedition | Conference | GREC | ||
Notes | DAG;600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RiF2017 | Serial | 3059 | ||
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Author | Oriol Vicente; Alicia Fornes; Ramon Valdes | ||||
Title | La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades | Type | Conference Article | ||
Year | 2017 | Publication | 3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional | Abbreviated Journal | |
Volume | Issue | Pages | 281-383 | ||
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ISSN | ISBN | 978-84-697-5692-8 | Medium | ||
Area | Expedition | Conference | HDH | ||
Notes | DAG; 600.121 | Approved | no | ||
Call Number | Admin @ si @ VFV2017 | Serial | 3060 | ||
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Author | Alicia Fornes; Beata Megyesi; Joan Mas | ||||
Title | Transcription of Encoded Manuscripts with Image Processing Techniques | Type | Conference Article | ||
Year | 2017 | Publication | Digital Humanities Conference | Abbreviated Journal | |
Volume | Issue | Pages | 441-443 | ||
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Area | Expedition | Conference | DH | ||
Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ FMM2017 | Serial | 3061 | ||
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Author | Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol | ||||
Title | The Robust Reading Competition Annotation and Evaluation Platform | Type | Conference Article | ||
Year | 2017 | Publication | 1st International Workshop on Open Services and Tools for Document Analysis | Abbreviated Journal | |
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Abstract | The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation
of data, and to provide online and offline performance evaluation and analysis services |
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Address | Kyoto; Japan; November 2017 | ||||
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Area | Expedition | Conference | ICDAR-OST | ||
Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ KGR2017 | Serial | 3063 | ||
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Author | Sergio Alloza; Flavio Escribano; Sergi Delgado; Ciprian Corneanu; Sergio Escalera | ||||
Title | XBadges. Identifying and training soft skills with commercial video games Improving persistence, risk taking & spatial reasoning with commercial video games and facial and emotional recognition system | Type | Conference Article | ||
Year | 2017 | Publication | 4th Congreso de la Sociedad Española para las Ciencias del Videojuego | Abbreviated Journal | |
Volume | 1957 | Issue | Pages | 13-28 | |
Keywords | Video Games; Soft Skills; Training; Skilling Development; Emotions; Cognitive Abilities; Flappy Bird; Pacman; Tetris | ||||
Abstract | XBadges is a research project based on the hypothesis that commercial video games (nonserious games) can train soft skills. We measure persistence, patial reasoning and risk taking before and after subjects paticipate in controlled game playing sessions.
In addition, we have developed an automatic facial expression recognition system capable of inferring their emotions while playing, allowing us to study the role of emotions in soft skills acquisition. We have used Flappy Bird, Pacman and Tetris for assessing changes in persistence, risk taking and spatial reasoning respectively. Results show how playing Tetris significantly improves spatial reasoning and how playing Pacman significantly improves prudence in certain areas of behavior. As for emotions, they reveal that being concentrated helps to improve performance and skills acquisition. Frustration is also shown as a key element. With the results obtained we are able to glimpse multiple applications in areas which need soft skills development. |
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Address | Barcelona; June 2017 | ||||
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Area | Expedition | Conference | COSECIVI; CEUR-WS | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ AED2017 | Serial | 3065 | ||
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Author | Jun Wan; Sergio Escalera; Gholamreza Anbarjafari; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Meysam Madadi; Juri Allik; Jelena Gorbova; Chi Lin; Yiliang Xie | ||||
Title | Results and Analysis of ChaLearn LAP Multi-modal Isolated and ContinuousGesture Recognition, and Real versus Fake Expressed Emotions Challenges | Type | Conference Article | ||
Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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Abstract | We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on “multimedia challenges beyond visual analysis”. In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field. | ||||
Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ WEA2017 | Serial | 3066 | ||
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