dc.identifier.uri |
http://dx.doi.org/10.15488/881 |
|
dc.identifier.uri |
http://www.repo.uni-hannover.de/handle/123456789/905 |
|
dc.contributor.author |
Chen, Lin
|
|
dc.contributor.author |
Rottensteiner, Franz
|
|
dc.contributor.author |
Heipke, Christian
|
|
dc.contributor.editor |
Paparoditis, N.
|
|
dc.contributor.editor |
Schindler, K.
|
|
dc.date.accessioned |
2016-12-21T10:56:35Z |
|
dc.date.available |
2016-12-21T10:56:35Z |
|
dc.date.issued |
2014 |
|
dc.identifier.citation |
Chen, L.; Rottensteiner, F.; Heipke, C.: Learning image descriptors for matching based on Haar features. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 40 (2014), Nr. 3, S. 61-66. DOI: https://doi.org/10.5194/isprsarchives-XL-3-61-2014 |
|
dc.description.abstract |
This paper presents a new and fast binary descriptor for image matching learned from Haar features. The training uses AdaBoost; the weak learner is built on response function for Haar features, instead of histogram-type features. The weak classifier is selected from a large weak feature pool. The selected features have different feature type, scale and position within the patch, having correspond threshold value for weak classifiers. Besides, to cope with the fact in real matching that dissimilar matches are encountered much more often than similar matches, cascaded classifiers are trained to motivate training algorithms see a large number of dissimilar patch pairs. The final trained output are binary value vectors, namely descriptors, with corresponding weight and perceptron threshold for a strong classifier in every stage. We present preliminary results which serve as a proof-of-concept of the work. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Göttingen : Copernicus GmbH |
|
dc.relation.ispartof |
ISPRS Technical Commission III Symposium : 5 – 7 September 2014, Zurich, Switzerland |
|
dc.relation.ispartofseries |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XL-3 |
|
dc.rights |
CC BY 3.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/3.0/ |
|
dc.subject |
AdaBoost |
eng |
dc.subject |
Descriptor learning |
eng |
dc.subject |
Haar features |
eng |
dc.subject |
Image descriptors |
eng |
dc.subject |
Image matching |
eng |
dc.subject |
Pooling configuration |
eng |
dc.subject |
Adaptive boosting |
eng |
dc.subject |
Cascaded classifiers |
eng |
dc.subject |
Corresponding weights |
eng |
dc.subject |
Descriptors |
eng |
dc.subject |
Response functions |
eng |
dc.subject |
Training algorithms |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
000 | Informatik, Informationswissenschaft, allgemeine Werke
|
ger |
dc.subject.ddc |
510 | Mathematik
|
ger |
dc.title |
Learning image descriptors for matching based on Haar features |
|
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9034 |
|
dc.relation.issn |
1682-1750 |
|
dc.relation.doi |
https://doi.org/10.5194/isprsarchives-XL-3-61-2014 |
|
dc.relation.doi |
https://doi.org/10.5194/isprsarchives-xl-3-61-2014 |
|
dc.bibliographicCitation.issue |
3 |
|
dc.bibliographicCitation.volume |
XL-3 |
|
dc.bibliographicCitation.firstPage |
61 |
|
dc.bibliographicCitation.lastPage |
66 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|