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Dedicated feature descriptor for outdoor augmented reality detection

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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 .

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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. doi:10.1109/38.963459

    Article  Google Scholar 

  2. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359. doi:10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  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. doi:10.1109/ICCV.2007.4409091

  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. doi:10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  6. Brown M, Hua G, Winder S (2011) Discriminative learning of local image descriptors. IEEE Trans Pattern Anal Mach Intell 33(1):43–57. doi:10.1109/TPAMI.2010.54

    Article  Google Scholar 

  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. doi:10.1109/TPAMI.1986.4767851

    Article  Google Scholar 

  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. doi:10.1145/1322432.1322434

    Article  Google Scholar 

  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. doi:10.1109/CVPR.2011.5995385

  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. doi:10.1109/TPAMI.2012.231

    Article  Google Scholar 

  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. doi:10.1109/ICRA.2015.7139445

  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. doi:10.1109/ICPR.2010.742

  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. doi:10.1109/ISPACS.2013.6704567

  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

    Google Scholar 

  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. doi:10.1109/TMM.2009.2032684

    Article  Google Scholar 

  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. doi:10.1007/978-0-387-84858-7

  20. Huang W, Wei Y, Xie Y, Jin H (2013) Survey of local invariant feature description. Chin Autom Congr (CAC) 2013:353–358. doi:10.1109/CAC.2013.6775758

    Google Scholar 

  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. doi:10.3390/s150922509. 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. doi:10.1109/ICCSE.2015.7250341

  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. doi:10.1109/ISMAR.2007.4538852

  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. doi:10.1109/IVS.2013.6629483

  25. Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10–12):1771–1787. doi:10.1016/j.neucom.2007.11.032. http://www.sciencedirect.com/science/article/pii/ S0925231208001124. Neurocomputing for Vision ResearchAdvances in Blind Signal Processing

  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. doi:10.1109/SITIS.2011.11

  27. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110. doi:10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  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. doi:10.1109/TPAMI.2005.188

    Article  Google Scholar 

  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. doi:10.1109/TPAMI.2009.23

    Article  Google Scholar 

  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. doi:10.1016/j.cviu.2014.03.009. 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

    Article  Google Scholar 

  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. doi:10.1109/CVPR.2009.5206550

  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. doi:10.1016/j.robot.2009.09.010

    Article  Google Scholar 

  36. Van de Sande KEA, Gevers T, Snoek CGM (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/TPAMI.2009.154

    Google Scholar 

  37. Verdie Y, Moo Yi K, Fua P, Lepetit V (2014) Tilde: A temporally invariant learned detector. ArXiv e-prints. doi:10.1109/CVPR.2015.7299165

  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. doi:10.1109/CVPR.2011.5995644

  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. doi:10.1109/ICCV.2011.6126294

  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. doi:10.1109/ICCE-China.2014.7029876

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Acknowledgments

I would like to thank the Consejo Nacional de Ciencia y Tecnología through the project number 340519 without whom this paper could not have been completed. Also, I would like to thank the Universidad Autnóma de Quértaro for its facilities and support.

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Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Las Campanas, 76010, Santiago de Querétaro, Querétaro, Mexico

    Andras Takacs, Manuel Toledano-Ayala, Jesus Carlos Pedraza-Ortega & Edgar A. Rivas-Araiza

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  1. Andras Takacs
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  2. Manuel Toledano-Ayala
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  3. Jesus Carlos Pedraza-Ortega
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  4. Edgar A. Rivas-Araiza
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Correspondence to Andras Takacs.

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Takacs, A., Toledano-Ayala, M., Pedraza-Ortega, J.C. et al. Dedicated feature descriptor for outdoor augmented reality detection. Pattern Anal Applic 21, 351–362 (2018). https://doi.org/10.1007/s10044-016-0581-8

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

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