Skip to main content
SpringerLink
Log in

A double circle structure descriptor and Hough voting matching for real-time object detection

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose real-time and reliable approaches for pose tracking of a rigid object by feature detection and image matching. We first present a new fast binary descriptor with a double circle structure of overlapping regions, namely double circle structure descriptor (DCSD). DCSD is rotation invariant and robust against blur, illumination changes, Joint Photographic Experts Group (JPEG) compression and orientation changes. Experimental results show that with fewer feature bits, DCSD is still discriminative and faster than the state-of-the-art features in many general situations. We then propose a new matching measure named Hough Voting Matching (HVM), which is based on clustering and Hough voting schemes. HVM can efficiently discriminate between correct and incorrect keypoint correspondences, and can be combined with some descriptors to improve the matching accuracy as an independent part. Experiments are also presented to illustrate that HVM can refine the matching results of DCSD if we embed HVM into a DCSD algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Rothganger F, Lazebnik S, Schmid C, Ponce J (2006) 3D object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. Int J Comput Vis 66(3):231–259

    Article  Google Scholar 

  2. Miao Q, Wang G, Shi C, Lin X, Ruan Z (2011) A new framework for on-line object tracking based on surf. Pattern Recogn Lett 32(13):1564–1571

    Article  Google Scholar 

  3. Brown M, Lowe DG (2003) Recognising panoramas. ICCV 3:1218

    Google Scholar 

  4. Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: Computer vision and pattern recognition, 2007, CVPR’07. IEEE Conference on, IEEE, pp 1—8

  5. Tavares JMR (2017) Analysis of biomedical images based on automated methods of image registration. In: Advances in visual computing. Springer, Berlin, pp 21–30

  6. Alves RS, Tavares JMR (2015) Computer image registration techniques applied to nuclear medicine images. In: Computational and experimental biomedical sciences: methods and applications. Springer, pp 173–191

  7. Oliveira FP, Tavares JMR (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93

    Article  Google Scholar 

  8. Hinterstoisser S, Lepetit V, Benhimane S, Fua P, Navab N (2011) Learning real-time perspective patch rectification. Int J Comput Vis 91(1):107–130

    Article  Google Scholar 

  9. Klein G, Murray D (2007) Parallel tracking and mapping for small ar workspaces. In: Mixed and augmented reality, 2007. ISMAR 2007. 6th IEEE and ACM international symposium on, IEEE, pp 225–234

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

    Article  Google Scholar 

  11. Ke Y, Sukthankar R (2004) Pca-sift: a more distinctive representation for local image descriptors. In: Computer vision and pattern recognition, 2004. CVPR 2004. In: Proceedings of the 2004 IEEE computer society conference on, vol 2, IEEE, pp II–506

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

    Article  Google Scholar 

  13. Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: Binary robust independent elementary features. Comput Vis ECCV 2010:778–792

    Google Scholar 

  14. Rublee E, Rabaud V, Konolige K, Bradski G (2011) Orb: an efficient alternative to sift or surf. In: Computer vision (ICCV), 2011 IEEE international conference on, IEEE, pp 2564–2571

  15. Muja M, Lowe DG (2012) Fast matching of binary features. In: Computer and robot vision (CRV), 2012 ninth conference on, IEEE, pp 404–410

  16. Qu X, Zhao F, Zhou M, Huo H (2014) A novel fast and robust binary affine invariant descriptor for image matching. Math Prob Eng

  17. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Computer vision–ECCV 2006.Springer, pp 430–443

  18. Wagner D, Reitmayr G, Mulloni A, Drummond T, Schmalstieg D (2008) Pose tracking from natural features on mobile phones. In: Proceedings of the 7th IEEE/ACM international symposium on mixed and augmented reality, IEEE Computer Society, pp 125–134

  19. Rosin PL (1999) Measuring corner properties. Comput Vis Image Underst 73(2):291–307

    Article  Google Scholar 

  20. Li L (2014) Image matching algorithm based on feature-point and daisy descriptor. J Multimed 9(6):829–834

    Article  Google Scholar 

  21. Liu K, Skibbe H, Schmidt T, Blein T, Palme K, Brox T, Ronneberger O (2014) Rotation-invariant hog descriptors using fourier analysis in polar and spherical coordinates. Int J Comput Vis 106(3):342–364

    Article  MathSciNet  MATH  Google Scholar 

  22. Qi X, Xiao R, Li C-G, Qiao Y, Guo J, Tang X (2014) Pairwise rotation invariant co-occurrence local binary pattern. Pattern Anal Mach Intell IEEE Trans 36(11):2199–2213

    Article  Google Scholar 

  23. Tola E, Lepetit V, Fua P (2008) A fast local descriptor for dense matching. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on, IEEE, pp 1–8

  24. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Anal Mach Intel IEEE Transa 24(7):971–987

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  26. Chen WC, Xiong Y, Gao J, Gelfand N, Grzeszczuk R (2007) Efficient extraction of robust image features on mobile devices. In: Proceedings of the 2007 6th IEEE and ACM international symposium on mixed and augmented reality, IEEE Computer Society, pp 1–2

  27. Zhang P, Sun Y, Shen H, Zhang R (2014) A parallel algorithm of pca-sift based on cuda. J Inf Comput Sci 11(9):3137–3147

    Article  Google Scholar 

  28. Leutenegger S, Chli M, Siegwart RY (2011) Brisk: binary robust invariant scalable keypoints. In: Computer vision (ICCV), 2011 IEEE international conference on, IEEE, pp 2548–2555

  29. Heinly J, Dunn E, FrahmJM (2012) Comparative evaluation of binary features. In: Computer vision–ECCV 2012, Springer, pp 759–773

  30. Datar M, Immorlica N,  Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the twentieth annual symposium on computational geometry, ACM, 2004, pp 253–262

  31. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  32. Li J, Yang T (2013) Efficient and robust feature matching via local descriptor generalized hough transform. In: Appl Mech Mater 373, Trans Tech Publ, pp 536–540

  33. Chen HY, Lin YY, Chen BY (2013) Robust feature matching with alternate hough and inverted hough transforms. In: Comput Vis Pattern Recog (CVPR), 2013 IEEE conference on, IEEE, pp 2762–2769

  34. Proença H (2014) Reigsac: fast discrimination of spurious keypoint correspondences on planar surfaces. Mach Visi Appl 25(3):763–773

    Article  Google Scholar 

  35. Kim J, Grauman K (2011) Boundary preserving dense local regions. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE conference on, IEEE, pp 1553–1560

  36. Bastos LF, Tavares JMR (2006) Matching of objects nodal points improvement using optimization. Inverse Probl Sci Eng 14(5):529–541

    Article  MATH  Google Scholar 

  37. Bastos LF, Tavares JMR (2004) Improvement of modal matching image objects in dynamic pedobarography using optimization techniques. In: Articulated motion and deformable objects. Springer, pp 39–50

  38. Oliveira FP, Tavares JMR (2009) Matching contours in images through the use of curvature, distance to centroid and global optimization with order-preserving constraint[J]

  39. Oliveira FP, Tavares JMR (2008) Algorithm of dynamic programming for optimization of the global matching between two contours defined by ordered points[J]

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

    Article  Google Scholar 

  41. Hinterstoisser S, Benhimane S, Navab N, Fua P, Lepetit V (2008) Online learning of patch perspective rectification for efficient object detection. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on, IEEE, pp 1–8

  42. Perronnin F, Sánchez J, Liu Y (2010) Large-scale image categorization with explicit data embedding. In: Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, IEEE, pp 2297–2304

  43. Tejani A, Tang D, Kouskouridas R, Kim TK (2014) Latent-class hough forests for 3D object detection and pose estimation. In: Computer vision–ECCV 2014. Springer, pp 462–477

  44. Wohlhart P, Lepetit V Learning descriptors for object recognition and 3d pose estimation. arXiv preprint arXiv:1502.05908

  45. Hinterstoisser S, Lepetit V, Ilic S, Holzer S, Bradski G, Konolige K, Navab N (2013) Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In: Computer vision–ACCV 2012, Springer, pp 548–562

  46. Brachmann E, Krull A, Michel F, Gumhold S, Shotton J, Rother C (2014) Learning 6D object pose estimation using 3D object coordinates. In: Computer vision–ECCV 2014, Springer, pp 536–551

  47. Zach C, Penate-Sanchez A, Pham MT (2015) A dynamic programming approach for fast and robust object pose recognition from range images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 196–203

  48. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) Slic superpixels compared to state-of-the-art superpixel methods. Pattern Anal Mach Intell IEEE Trans 34(11):2274–2282

    Article  Google Scholar 

  49. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Van Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1–2):43–72

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Shuang Ye & Chuancai Liu

  2. School of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Shuang Ye

  3. Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau, China

    Zhiwu Li

  4. School of Electro-Mechanical Engineering, Xidian University, Xi’an, 710071, China

    Zhiwu Li

Authors
  1. Shuang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuancai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiwu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuang Ye.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, S., Liu, C. & Li, Z. A double circle structure descriptor and Hough voting matching for real-time object detection. Pattern Anal Applic 19, 1143–1157 (2016). https://doi.org/10.1007/s10044-016-0539-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-016-0539-x

Keywords

Search

Navigation