| |
Records |
Links |
|
Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
|
| |
Title |
Traffic sign recognition system with β -correction |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
|
| |
Volume |
21 |
Issue |
2 |
Pages |
99–111 |
|
| |
Keywords |
|
|
| |
Abstract |
Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher  |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0932-8092 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HUPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ EPR2010a |
Serial |
1276 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
|
| |
Title |
Personalization and User Verification in Wearable Systems using Biometric Walking Patterns |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
Personal and Ubiquitous Computing |
Abbreviated Journal |
PUC |
|
| |
Volume |
16 |
Issue |
5 |
Pages |
563-580 |
|
| |
Keywords |
|
|
| |
Abstract |
In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1617-4909 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB;HuPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CPR2012 |
Serial |
1706 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
|
| |
Title |
Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
|
| |
Volume |
18 |
Issue |
2 |
Pages |
247-261 |
|
| |
Keywords |
Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification |
|
| |
Abstract |
In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1433-7541 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ PEP2013 |
Serial |
2251 |
|
| Permanent link to this record |
