| Records |
| Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
| Title |
A non-rigid appearance model for shape description and recognition |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
45 |
Issue |
9 |
Pages |
3105--3113 |
Keywords  |
Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition |
| Abstract |
In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. |
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0031-3203 |
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DAG |
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no |
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DAG @ dag @ AFV2012 |
Serial |
1982 |
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| Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
| Title |
Monocular Egomotion Estimation based on Image Matching |
Type |
Conference Article |
| Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
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| Volume |
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Issue |
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Pages |
425-430 |
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SLAM |
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Portugal |
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ICPRAM |
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ADAS |
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no |
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Admin @ si @ CPL2012a;; ADAS @ adas @ |
Serial |
2011 |
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| Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
| Title |
Visual SLAM for Driverless Cars: A Brief Survey |
Type |
Conference Article |
| Year |
2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
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Pages |
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SLAM |
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Alcalá de Henares |
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IVW |
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ADAS |
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no |
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Admin @ si @ RSP2012; ADAS @ adas |
Serial |
2019 |
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| Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
| Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
45 |
Issue |
9 |
Pages |
3072-3083 |
| Keywords |
Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
| Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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0031-3203 |
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DAG |
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no |
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Admin @ si @ GVB2012a |
Serial |
1992 |
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| Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
| Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
33 |
Issue |
15 |
Pages |
1980–1990 |
| Keywords |
Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
| Abstract |
Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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DAG |
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no |
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Admin @ si @ GVB2012b |
Serial |
1993 |
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| Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
| Title |
Pedestrian Detection: Exploring Virtual Worlds |
Type |
Book Chapter |
| Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
145-162 |
| Keywords |
Virtual worlds; Pedestrian Detection; Domain Adaptation |
| Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
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iConcept Press |
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English |
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ISBN |
978-1-477554-82-1 |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MGV2012 |
Serial |
1979 |
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| Author |
David Masip; Alexander Todorov; Jordi Vitria |
| Title |
The Role of Facial Regions in Evaluating Social Dime |
Type |
Conference Article |
| Year |
2012 |
Publication |
12th European Conference on Computer Vision – Workshops and Demonstrations |
Abbreviated Journal |
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| Volume |
7584 |
Issue |
II |
Pages |
210-219 |
| Keywords |
Workshops and Demonstrations |
| Abstract |
Facial trait judgments are an important information cue for people. Recent works in the Psychology field have stated the basis of face evaluation, defining a set of traits that we evaluate from faces (e.g. dominance, trustworthiness, aggressiveness, attractiveness, threatening or intelligence among others). We rapidly infer information from others faces, usually after a short period of time (< 1000ms) we perceive a certain degree of dominance or trustworthiness of another person from the face. Although these perceptions are not necessarily accurate, they influence many important social outcomes (such as the results of the elections or the court decisions). This topic has also attracted the attention of Computer Vision scientists, and recently a computational model to automatically predict trait evaluations from faces has been proposed. These systems try to mimic the human perception by means of applying machine learning classifiers to a set of labeled data. In this paper we perform an experimental study on the specific facial features that trigger the social inferences. Using previous results from the literature, we propose to use simple similarity maps to evaluate which regions of the face influence the most the trait inferences. The correlation analysis is performed using only appearance, and the results from the experiments suggest that each trait is correlated with specific facial characteristics. |
| Address |
Florence, Italy |
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Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Andrea Fusiello, Vittorio Murino, Rita Cucchiara |
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LNCS |
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0302-9743 |
ISBN |
978-3-642-33867-0 |
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ECCVW |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ MTV2012 |
Serial |
2171 |
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