More Web Proxy on the site http://driver.im/

article

Dedicated feature descriptor for outdoor augmented reality detection

Authors:

Manuel Toledano-Ayala,

Jesus Carlos Pedraza-Ortega,

Edgar A. Rivas-AraizaAuthors Info & Claims

Pattern Analysis & Applications, Volume 21, Issue 2

Pages 351 - 362

https://doi.org/10.1007/s10044-016-0581-8

Published: 01 May 2018 Publication History

Abstract

Stable augmented reality applications consist of an accurate registration supported by a robust tracking module. In outdoor locations, the changing environmental and light conditions compromise this tracking. Reliable descriptors under unsettled conditions are essential for this process. The most used descriptors have this distinctive capacity, but computers and mobile devices process them in a long time frame. This paper investigates a new lightweight environment dedicated descriptor (EDD) trained with a machine-learning algorithm. The descriptor analyzes the scene characteristics with elements that can be computed fast and that have distinctive information about the selected area. The complete descriptor is used for semantic feature extraction with the aid of a trained random forest classifier. The descriptor is compared with the most popular descriptors--with respect to speed, accuracy, and invariance to illumination changes, scale, affine transformation, and rotation--and the results show that it is faster and in most cases equally reliable .

References

[1]

Azuma R, Baillot Y, Behringer R, Feiner S, Julier S, MacIntyre B (2001) Recent advances in augmented reality. IEEE Comput Graph Appl 21(6):34---47.

Digital Library

[2]

Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346---359.

Digital Library

[3]

Berg A, Grabler F, Malik J (2007) Parsing images of architectural scenes. In: IEEE 11th International Conference on Computer vision. ICCV 2007. pp 1---8.

[4]

Bradski G (2000) The opencv library. Dr. Dobb's J Softw Tools

[5]

Breiman L (2001) Random forests. Mach Learn 45(1):5---32.

Digital Library

[6]

Brown M, Hua G, Winder S (2011) Discriminative learning of local image descriptors. IEEE Trans Pattern Anal Mach Intell 33(1):43---57.

Digital Library

[7]

Brust C, Sickert S, Simon M, Rodner E, Denzler J (2015) Convolutional patch networks with spatial prior for road detection and urban scene understanding. CoRR. abs/1502.06344. http://arxiv.org/abs/1502.06344

[8]

Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell PAMI---8(6):679---698.

Digital Library

[9]

Chu DM, Smeulders AWM (2010) Color invariant surf in discriminative object tracking. In: ECCV Workshop on Color and Reflectance in Imaging and Computer Vision. http://www.science.uva.nl/research/publications/2010/ ChuCRICV2010

[10]

Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: In Workshop on Statistical Learning in Computer Vision, ECCV, pp 1---22. http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.72.604

[11]

Fabbri R, Costa LDF, Torelli JC, Bruno OM (2008) 2D euclidean distance transform algorithms: a comparative survey. ACM Comput Surv 40(1):2:1---2:44.

Digital Library

[12]

Fan B, Wu F, Hu Z (2011) Aggregating gradient distributions into intensity orders: A novel local image descriptor. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2377---2384.

Digital Library

[13]

Farabet C, Couprie C, Najman L, LeCun Y (2013) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915---1929.

Digital Library

[14]

Feng G, Liu Y, Liao Y (2015) Loind: An illumination and scale invariant rgb-d descriptor. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp 1893---1898.

[15]

Fröhlich B, Rodner E, Denzler J (2010) A fast approach for pixelwise labeling of facade images. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp 3029---3032.

Digital Library

[16]

Fujiwara Y, Okamoto T, Kondo K (2013) Sift feature reduction based on feature similarity of repeated patterns. In: 2013 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS), pp 311---314.

[17]

Gool LV, Zeng G, den Borre FV, Müller P (2007) Towards mass-produced building models. In: Stilla U, Mayer H, Rottensteiner F, Heipke C, Hinz S (eds) Photogrammetric image analysis. Institute of Photogrammetry and Cartography, Technische Universitaet Muenchen, Munich, pp 209---220

[18]

Guan T, Wang C (2009) Registration based on scene recognition and natural features tracking techniques for wide-area augmented reality systems. IEEE Trans Multimedia 11(8):1393---1406.

Digital Library

[19]

Hastie T, Tibshirani R, Friedman J (2009) Random forests. In: The elements of statistical learning: data mining, inference, and prediction, vol 2019, pp 587---604.

[20]

Huang W, Wei Y, Xie Y, Jin H (2013) Survey of local invariant feature description. Chin Autom Congr (CAC) 2013:353---358.

[21]

Jiang G, Liu L, Zhu W, Yin S, Wei S (2015) A 181 gops akaze accelerator employing discrete-time cellular neural networks for real-time feature extraction. Sensors 15(9):22,509. http://www.mdpi.com/1424-8220/15/9/22509

[22]

Jihua Y, Shuxia S, Yahui C (2015) A face recognition algorithm based on lle-sift feature descriptors. In: 2015 10th International Conference on Computer Science Education (ICCSE), pp 729---734.

[23]

Klein G, Murray D (2007) Parallel tracking and mapping for small ar workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, 2007. ISMAR 2007. pp 225---234.

Digital Library

[24]

Lategahn H, Beck J, Kitt B, Stiller C (2013) How to learn an illumination robust image feature for place recognition. In: Intelligent Vehicles Symposium (IV), 2013 IEEE, pp 285---291.

[25]

Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10---12):1771---1787. http://www.sciencedirect.com/science/article/pii/ S0925231208001124. Neurocomputing for Vision ResearchAdvances in Blind Signal Processing

Digital Library

[26]

Liu J, Liang X (2011) I-brief: A fast feature point descriptor with more robust features. In: 2011 Seventh International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), pp 322---328.

Digital Library

[27]

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91---110.

Digital Library

[28]

McManus C, Upcroft B, Newmann P (2014) Scene signatures : localised and point-less features for localisation. In: Robotics: Science and Systems X. University of California, Berkeley, CA. http://eprints.qut.edu.au/76158/

[29]

Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615---1630.

Digital Library

[30]

Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp 2681---2684

[31]

Ozuysal M, Calonder M, Lepetit V, Fua P (2010) Fast keypoint recognition using random ferns. IEEE Trans Pattern Anal Mach Intell 32(3):448---461.

Digital Library

[32]

Rigamonti R, Lepetit V, Gonzlez G, Tretken E, Benmansour F, Brown M, Fua P (2014) On the relevance of sparsity for image classification. Comput Vision Image Underst 125:115---127. http://www.sciencedirect.com/science/article/pii/ S1077314214000757

[33]

Russell BC, Torralba A, Murphy KP, Freeman WT (2008) Labelme: a database and web-based tool for image annotation. Int J Comput Vision 77(1---3):157---173

Digital Library

[34]

Tang F, Lim SH, Chang N, Tao H (2009) A novel feature descriptor invariant to complex brightness changes. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pp. 2631---2638.

[35]

Valgren C, Lilienthal AJ (2010) Sift, SURF and seasons: appearance-based long-term localization in outdoor environments. Robot Auton Syst 58(2):149---156.

Digital Library

[36]

Van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell.

Digital Library

[37]

Verdie Y, Moo Yi K, Fua P, Lepetit V (2014) Tilde: A temporally invariant learned detector. ArXiv e-prints.

[38]

Wang J, Tan Y (2011) Efficient euclidean distance transform using perpendicular bisector segmentation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1625---1632.

Digital Library

[39]

Wang Z, Fan B, Wu F (2011) Local intensity order pattern for feature description. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 603---610.

Digital Library

[40]

Xiao P, Cai N, Tang B, Weng S, Wang H (2014) Efficient sift descriptor via color quantization. In: 2014 IEEE International Conference on Consumer Electronics - China, pp 1---3.

Cited By

Zhong Z(2024)Research on Product Advertising Design Combining Feature Extraction Technology and Web3D TechnologyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360894823:6(1-13)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3608948
Zhong Z(2023)Research on the Implementation of Advertising Design Teaching Based on Unity3D Development Platform and Web3D TechnologyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359529423:6(1-14)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3595294
Tian LZhou J(2022)Real Time Augmented Reality Tracking Registration Based on Motion Blur Template Matching Image Construction ModelMobile Networks and Applications10.1007/s11036-021-01816-327:3(874-885)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11036-021-01816-3

Index Terms

Dedicated feature descriptor for outdoor augmented reality detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Graphics systems and interfaces
      1. Virtual reality
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

Recommendations

G2P: a new descriptor for pedestrian detection
Abstract
Pedestrian detection plays an important role in many applications. Since its birth 13 years ago, Histogram Of Gradient (HOG) descriptor has become a popular descriptor for pedestrian detection. Besides its original instantiation, the HOG also ...
Binary Feature Descriptor for Omnidirectional Images Processing
IPAC '15: Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication

An omnidirectional image has a 360° view around a viewpoint and which could be applied in a variety of fields, such as autonomous navigation, surveillance systems, virtual reality and remote monitoring, is presented. Many techniques of digital image ...
A rotationally invariant descriptor based on mixed intensity feature histograms

The LIOP (Local Intensity Order Pattern) operator sorts the intensity of neighboring points around one sample point and encodes an intensity order information into this sampling point. In the experiments, we found that the relative order information of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Pattern Analysis & Applications

Pattern Analysis & Applications Volume 21, Issue 2

May 2018

313 pages

ISSN:1433-7541

EISSN:1433-755X

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer-Verlag London Ltd., part of Springer Nature.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 May 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhong Z(2024)Research on Product Advertising Design Combining Feature Extraction Technology and Web3D TechnologyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360894823:6(1-13)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3608948
Zhong Z(2023)Research on the Implementation of Advertising Design Teaching Based on Unity3D Development Platform and Web3D TechnologyACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359529423:6(1-14)Online publication date: 5-May-2023
https://dl.acm.org/doi/10.1145/3595294
Tian LZhou J(2022)Real Time Augmented Reality Tracking Registration Based on Motion Blur Template Matching Image Construction ModelMobile Networks and Applications10.1007/s11036-021-01816-327:3(874-885)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1007/s11036-021-01816-3

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents