Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2015 | OriginalPaper | Chapter

1. Introduction

Authors : Bin Fan, Zhenhua Wang, Fuchao Wu

Published in: Local Image Descriptor: Modern Approaches

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Local image descriptor is a kind of vector (either in float type or binary type) used as the signature of a local image. The aim of this representation is to make the local image as distinctive as possible while maintaining robustness to various kinds of image transformations, both photometric and geometric ones, including viewpoint changes (out-plane rotation), scale changes, in-plane rotation, image blur, noise, illumination, etc. By achieving these characteristics, one can easily establish correspondences among images of the same scene taken from different positions, or among similar images.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Classical Local Descriptors
Literature
1.
Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a day. In: International Conference on Computer Vision, pp. 72–79 (2009)
2.
Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: Fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517 (2012)
3.
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef
4.
Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., Fua, P.: BRIEF: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 33(7), 1281–1298 (2012)CrossRef
5.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
6.
Fan, B., Kong, Q., Trzcinski, T., Wang, Z., Pan, C., Fua, P.: Receptive fields selection for binary feature description. IEEE Trans. Image Process. 23(6), 2583–2595 (2014)MathSciNetCrossRef
7.
Fan, B., Wu, F., Hu, Z.: Rotationally invariant descriptors using intensity order pooling. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2031–2045 (2012)CrossRef
8.
Leutenegger, S., Chli, M., Siegwart, R.: BRISK: Binary robust invariant scalable keypoints. In: International Conference on Computer Vision, pp. 2548–2555 (2011)
9.
Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081 (2012)
10.
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRef
11.
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRef
12.
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29, 51–59 (1996)CrossRef
13.
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
14.
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, pp. 2564–2571 (2011)
15.
Sironi, A., Tekin, B., Rigamonti, R., Lepetit, V., Fua, P.: Learning separable filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 94–106 (2015)CrossRef
16.
Szeliski, R.: Image alignment and stitching: a tutorial. Found. Trends Comput. Graph. Vis. 2, 1–104 (2006)CrossRef
17.
Trzcinski, T., Christoudias, M., Fua, P., Lepetit, V.: Boosting binary keypoint descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874–2881 (2013)
18.
Trzcinski, T., Lepetit, V.: Efficient discriminative projections for compact binary descriptors. In: European Conference on Computer Vision, pp. 228–242 (2012)
19.
Wang, Z., Fan, B., Wu, F.: Local intensity order pattern for feature description. In: International Conference on Computer Vision, pp. 603–610 (2011)
20.
Wang, Z., Fan, B., Wu, F.: FRIF: Fast robust invariant feature. In: British Machine Vision Conference (2013)
21.
Xu, X., Tian, L., Feng, J., Zhou, J.: OSRI: a rotationally invariant binary descriptor. IEEE Trans. Image Process. 23(7), 2983–2995 (2014)MathSciNetCrossRef
22.
Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)CrossRef
Metadata
Title
Introduction
Authors
Bin Fan
Zhenhua Wang
Fuchao Wu
Copyright Year
2015
Publisher
Springer Berlin Heidelberg
Book
Local Image Descriptor: Modern Approaches

Print ISBN: 978-3-662-49171-3

Electronic ISBN: 978-3-662-49173-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-662-49173-7_1

Premium Partner

    Image Credits