|
Records |
Links |
|
Author |
David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Traffic sign recognition for computer vision project-based learning |
Type ![sorted by Type field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Journal Article |
|
Year |
2013 |
Publication |
IEEE Transactions on Education |
Abbreviated Journal |
T-EDUC |
|
|
Volume |
56 |
Issue |
3 |
Pages |
364-371 |
|
|
Keywords |
traffic signs |
|
|
Abstract |
This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0018-9359 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; CIC |
Approved |
no |
|
|
Call Number |
Admin @ si @ GSL2013; ADAS @ adas @ |
Serial |
2160 |
|
Permanent link to this record |
|
|
|
|
Author |
Joan Serrat; Felipe Lumbreras; Antonio Lopez |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Cost estimation of custom hoses from STL files and CAD drawings |
Type ![sorted by Type field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Journal Article |
|
Year |
2013 |
Publication |
Computers in Industry |
Abbreviated Journal |
COMPUTIND |
|
|
Volume |
64 |
Issue |
3 |
Pages |
299-309 |
|
|
Keywords |
On-line quotation; STL format; Regression; Gaussian process |
|
|
Abstract |
We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.057; 600.054; 605.203 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SLL2013; ADAS @ adas @ |
Serial |
2161 |
|
Permanent link to this record |
|
|
|
|
Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Occlusion handling via random subspace classifiers for human detection |
Type ![sorted by Type field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Journal Article |
|
Year |
2014 |
Publication |
IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
|
|
Volume |
44 |
Issue |
3 |
Pages |
342-354 |
|
|
Keywords |
Pedestriand Detection; occlusion handling |
|
|
Abstract |
This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2168-2267 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ MVL2014 |
Serial |
2213 |
|
Permanent link to this record |
|
|
|
|
Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
![download PDF file pdf](http://refbase.cvc.uab.es/img/file_PDF.gif)
![find book details (via ISBN) isbn](http://refbase.cvc.uab.es/img/isbn.gif)
|
|
Title |
Learning a Part-based Pedestrian Detector in Virtual World |
Type ![sorted by Type field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Journal Article |
|
Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
|
|
Volume |
15 |
Issue |
5 |
Pages |
2121-2131 |
|
|
Keywords |
Domain Adaptation; Pedestrian Detection; Virtual Worlds |
|
|
Abstract |
Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ XVL2014 |
Serial |
2433 |
|
Permanent link to this record |
|
|
|
|
Author |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title |
Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm |
Type ![sorted by Type field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Journal Article |
|
Year |
2013 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
EXWA |
|
|
Volume |
40 |
Issue |
17 |
Pages |
6707-6712 |
|
|
Keywords |
Neural gas; Expert vision; Eye-tracking; Fixations |
|
|
Abstract |
Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0957-4174 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ CRM2013 |
Serial |
2438 |
|
Permanent link to this record |