Abstract
This paper proposes a novel multiscale 3D keypoint detection method via the double Gaussian weighted dissimilarity measure. At each scale, the shape index value and the double Gaussian weighted dissimilarity measure value of each 3D point are firstly computed. Then the candidate keypoints with local maximum dissimilarity measure values are determined. Finally the final 3D keypoints are detected under our proposed multiscale detection scheme. As the dissimilarity measure used in this paper has better robust descriptive ability and is rotation and translation transformation invariant, the proposed detection method is robust to noise, rotation and translation transformation. Extensive experimental results have shown that using our proposed multiscale detection method, we can detect the keypoints with higher repeatability under different noise levels.
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References
Campbell RJ, Flynn PJ (2001) A survey of free-form object representation and recognition techniques. Comput Vis Image Underst 81(2):166–210
Castellani U, Cristani M, Fantoni S, Murino V (2008) Sparse points matching by combining 3D mesh saliency with statistical descriptors. Eurographics 27(2):643–652
Chen H, Bhanu B (2007) Human ear recognition in 3D. IEEE Trans Pattern Anal Mach Intell 29(4):718–737
Dong M, Chen Y (2008) Salient region detection and feature extraction in 3D visual data, in proceedings of the IEEE international conference on image processing. San Diego, CA, USA
Dorai C, Jain AK (1997) Cosmos - a representation scheme for 3D free-form objects. IEEE Trans Pattern Anal Mach Intel 19(10):1115–1130
Dutagaci H, Cheung CP, Godil A (2011) Evaluation of 3D interest point detection techniques. In: Proceedings of Eurographics workshop on 3D object retrieval. Llandudno, UK, pp. 57–64
Dutagaci H, Cheung CP, Godil A (2012) Evaluation of 3D interest point detection techniques via human-generated ground truth. Int J Comput Graph 28(9):901–917
Gelfand N, Mitra N, Guibas L, Pottmann H (2005) Robust global registration, Eurographics Symposium on Geometry Processing
Gomb P (2009) Detection of interest points on 3d data: extending the Harris operator. Comput Recog Syst 3, AISC 57:103–111
Ho HT, Gibbins D (2008a) Multiscale feature extraction for 3D surface registration using local shape variation. Int Conf Image Vis Comput, pp. 1–6
Ho HT, Gibbins D (2008b) Multiscale feature extraction for 3D models using local surface curvature. Digital Image Computing, Techniques and Applications, pp. 16–23
Huang QX, Flöry S, Gelfand N et al (2006) Reassembling fractured objects by geometric matching. ACM Trans Graph (TOG) 25(3):569–578
Koenderink JJ, van Doorn AJ (1992) Surface shape and curvature scales. Image Vision Comput 10:557–565
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Mian A, Bennamoun M, Owens R (2008) Salient point detection and local feature matching for textured 3D face recognition. Int J Comput Vis 79(1):1–12
Mian A, Bennamoun M, Owens R (2010) On the repeatability and quality of salient points for local feature-based 3D object retrieval from cluttered scenes. Int J Comput Vis 89(2–3):348–361
Salti S, Tombari F, & Stefano LD (2011) A performance evaluation of 3d salient point detectors. In Proceedings of the 2011 international conference on 3D imaging, modeling, processing, visualization and transmission, pp. 236–243
Vikstén F, Nordberg K, Kalms M (2008) Point-of-interest detection for range data. In Proceedings of the international conference on pattern recognition, pp. 1–4
Yu T-H, Oliver J (2013) Woodford, Roberto Cipolla, a performance evaluation of volumetric 3D interest point. Int J Comput Vis 102(1–3):180–197
Zeng H, Zhang B, Mu Z, Wu, H, Wang X, (2013) Local weighted dissimilarity measure based multiscale 3D keypoint detection. IEEE Seventh International Conference on Image and Graphics (ICIG), pp 302–306
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This article is supported by the National Natural Science Foundation of China (Grant No. 61375010 and No. 61005009).
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Zeng, H., Wang, H. & Dong, J. Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure. Multimed Tools Appl 76, 26377–26389 (2017). https://doi.org/10.1007/s11042-016-4139-y
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DOI: https://doi.org/10.1007/s11042-016-4139-y