| Records |
| Author |
Hana Jarraya; Oriol Ramos Terrades; Josep Llados |
| Title |
Learning structural loss parameters on graph embedding applied on symbolic graphs |
Type |
Conference Article |
| Year |
2017 |
Publication  |
12th IAPR International Workshop on Graphics Recognition |
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We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset. |
| Address |
Kyoto; Japan; November 2017 |
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GREC |
| Notes |
DAG; 600.097; 600.121 |
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no |
| Call Number |
Admin @ si @ JRL2017b |
Serial |
3073 |
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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 |
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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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GREC |
| Notes |
DAG;600.097; 600.121 |
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no |
| Call Number |
Admin @ si @ RiF2017 |
Serial |
3059 |
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| Author |
Pau Riba; Josep Llados; Alicia Fornes |
| Title |
Error-tolerant coarse-to-fine matching model for hierarchical graphs |
Type |
Conference Article |
| Year |
2017 |
Publication |
11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition |
Abbreviated Journal |
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| Volume |
10310 |
Issue |
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Pages |
107-117 |
| Keywords |
Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching |
| Abstract |
Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting. |
| Address |
Anacapri; Italy; May 2017 |
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Springer International Publishing |
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Editor |
Pasquale Foggia; Cheng-Lin Liu; Mario Vento |
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GbRPR |
| Notes |
DAG; 600.097; 601.302; 600.121 |
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no |
| Call Number |
Admin @ si @ RLF2017a |
Serial |
2951 |
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| Author |
Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate |
| Title |
Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study |
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Conference Article |
| Year |
2017 |
Publication |
11th European CytoGenesis Conference |
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Florencia; Italia; July 2017 |
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ECA |
| Notes |
IAM; 600.096; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ SBG2017a |
Serial |
2936 |
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| Author |
Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez |
| Title |
Computer Vision in Vehicle Technology: Land, Sea & Air |
Type |
Book Whole |
| Year |
2017 |
Publication |
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Issue |
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Pages |
161-163 |
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Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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John Wiley & Sons, Ltd |
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978-1-118-86807-2 |
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| Notes |
ADAS; 600.118 |
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no |
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Admin @ si @ LIP2017a |
Serial |
2937 |
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| Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
| Title |
Challenges in Multi-modal Gesture Recognition |
Type |
Book Chapter |
| Year |
2017 |
Publication |
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Issue |
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Pages |
1-60 |
| Keywords |
Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM |
| Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
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HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ EAG2017 |
Serial |
3008 |
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| Author |
Laura Igual; Santiago Segui |
| Title |
Introduction to Data Science – A Python Approach to Concepts, Techniques and Applications. Undergraduate Topics in Computer Science |
Type |
Book Whole |
| Year |
2017 |
Publication |
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1-215 |
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978-3-319-50016-4 |
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978-3-319-50016-4 |
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MILAB |
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no |
| Call Number |
Admin @ si @ IgS2017 |
Serial |
3027 |
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