|
Records |
Links |
|
Author |
Sergio Vera; Debora Gil; Antonio Lopez; Miguel Angel Gonzalez Ballester |
![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 |
Multilocal Creaseness Measure |
Type |
Journal |
|
Year |
2012 |
Publication |
The Insight Journal |
Abbreviated Journal |
IJ |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Ridges, Valley, Creaseness, Structure Tensor, Skeleton, |
|
|
Abstract |
This document describes the implementation using the Insight Toolkit of an algorithm for detecting creases (ridges and valleys) in N-dimensional images, based on the Local Structure Tensor of the image. In addition to the filter used to calculate the creaseness image, a filter for the computation of the structure tensor is also included in this submission. |
|
|
Address |
|
|
|
Corporate Author |
Alma IT Systems |
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
english |
Summary Language |
english |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN ![sorted by ISSN field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
IAM;ADAS; |
Approved |
no |
|
|
Call Number |
IAM @ iam @ VGL2012 |
Serial |
1840 |
|
Permanent link to this record |
|
|
|
|
Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
![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 |
Multispectral Piecewise Planar Stereo using Manhattan-World Assumption |
Type |
Journal Article |
|
Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
|
Volume |
34 |
Issue |
1 |
Pages |
52-61 |
|
|
Keywords |
Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images |
|
|
Abstract |
This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. |
|
|
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 ![sorted by ISSN field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.054; 600.055; 605.203 |
Approved |
no |
|
|
Call Number |
Admin @ si @ BLS2013 |
Serial |
2245 |
|
Permanent link to this record |
|
|
|
|
Author |
Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo |
![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 |
Multispectral Image Feature Points |
Type |
Journal Article |
|
Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
|
Volume |
12 |
Issue |
9 |
Pages |
12661-12672 |
|
|
Keywords |
multispectral image descriptor; color and infrared images; feature point descriptor |
|
|
Abstract |
Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art. |
|
|
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 ![sorted by ISSN field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ ABL2012 |
Serial |
2154 |
|
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 |
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 ![sorted by ISSN field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
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 |
Monica Piñol; Angel Sappa; Ricardo Toledo |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
|
|
Title |
Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
|
|
Volume |
150 |
Issue |
A |
Pages |
106–115 |
|
|
Keywords |
Reinforcement learning; Q-learning; Bag of features; Descriptors |
|
|
Abstract |
This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. |
|
|
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 ![sorted by ISSN field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.055; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ PST2015 |
Serial |
2473 |
|
Permanent link to this record |