|
Records |
Links |
|
Author |
Hannes Mueller; Andre Groeger; Jonathan Hersh; Andrea Matranga; Joan Serrat |
![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 ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Monitoring war destruction from space using machine learning |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Proceedings of the National Academy of Sciences of the United States of America |
Abbreviated Journal |
PNAS |
|
|
Volume |
118 |
Issue |
23 |
Pages |
e2025400118 |
|
|
Keywords |
|
|
|
Abstract |
Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available. |
|
|
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 |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ MGH2021 |
Serial |
3584 |
|
Permanent link to this record |
|
|
|
|
Author |
Jaume Amores |
![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 ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
MILDE: multiple instance learning by discriminative embedding |
Type |
Journal Article |
|
Year |
2015 |
Publication |
Knowledge and Information Systems |
Abbreviated Journal |
KAIS |
|
|
Volume |
42 |
Issue |
2 |
Pages |
381-407 |
|
|
Keywords |
Multi-instance learning; Codebook; Bag of words |
|
|
Abstract |
While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer London |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0219-1377 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 601.042; 600.057; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Amo2015 |
Serial |
2383 |
|
Permanent link to this record |
|
|
|
|
Author |
Aura Hernandez-Sabate; Meritxell Joanpere; Nuria Gorgorio; Lluis Albarracin |
![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 ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Mathematics learning opportunities when playing a Tower Defense Game |
Type |
Journal |
|
Year |
2015 |
Publication |
International Journal of Serious Games |
Abbreviated Journal |
IJSG |
|
|
Volume |
2 |
Issue |
4 |
Pages |
57-71 |
|
|
Keywords |
Tower Defense game; learning opportunities; mathematics; problem solving; game design |
|
|
Abstract |
A qualitative research study is presented herein with the purpose of identifying mathematics learning opportunities in students between 10 and 12 years old while playing a commercial version of a Tower Defense game. These learning opportunities are understood as mathematicisable moments of the game and involve the establishment of relationships between the game and mathematical problem solving. Based on the analysis of these mathematicisable moments, we conclude that the game can promote problem-solving processes and learning opportunities that can be associated with different mathematical contents that appears in mathematics curricula, thought it seems that teacher or new game elements might be needed to facilitate the processes. |
|
|
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 |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ HJG2015 |
Serial |
2730 |
|
Permanent link to this record |
|
|
|
|
Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras |
![find record details (via OpenURL) openurl](http://refbase.cvc.uab.es/img/xref.gif)
|
|
Title ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Los faros delanteros a traves del objetivo |
Type |
Journal |
|
Year |
2008 |
Publication |
UAB Divulga, Revista de divulgacion cientifica |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
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 |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ SDL2008b |
Serial |
1471 |
|
Permanent link to this record |
|
|
|
|
Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
![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 ![sorted by Title field, descending order (down)](http://refbase.cvc.uab.es/img/sort_desc.gif) |
Learning to measure for preshipment garment sizing |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
|
|
Volume |
130 |
Issue |
|
Pages |
327-339 |
|
|
Keywords |
Apparel; Computer vision; Structured prediction; Regression |
|
|
Abstract |
Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
|
|
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 |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; MSIAU; 600.122; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SLR2018 |
Serial |
3128 |
|
Permanent link to this record |