Chuin-Mu Wang*

Department of Computer Science and Information Engineering, National Chinyi, University of Technology, Taiping, Taiwan. *Correponding author. E-mail address: cmwang@ncut.edu.tw.

Abstract

The surface deformation detection of an object has been a very popular research project in recent years; in human vision, we can easily detect the location of the target and that scale of the surface rotation, and change of the viewpoint makes the surface deformation, but in a vision of the computer is a challenge. In those backgrounds of questions, we can propose a framework that is the surface deformation, which is based on the detection method of BRIEF to calculate object surface deformation. But BRIEF calculation has some problem that can't rotate and change character; we also propose a useful calculation method to solve the problem, and the method proved by experiment can overcome the problem, by the way, it's very useful. The average operation time every picture in continuous image is 50~80 ms in 2.5 GHz computer, let us look back for some related estimation technology of surface deformation, and there are still a few successful project that is surface deformation detection in the document.

Keywords: BRIEF descriptor; Binary descriptor; Non-rigid surface; Deformation detection.

Reference

Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). FREAK: Fast Retina Keypoint. IEEE Computer Vision and Pattern Recognition (pp. 510-517). http://dx.doi.org/10.1109/CVPR.2012.6247715        [ Links ]

Bay, H., Tuytelaars, T., & Gool, L.V. (2006). SURF: Speeded Up Robust Features. In European Conference on Computer Vision (vol. 3951, pp. 404-417).         [ Links ]

Baker, S., & Matthews, I. (2004). Lucas-Kanade 20 Years On: A Unifying Framework. International Journal of Computer Vision, 56, 221-255).         [ Links ]

Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010). BRIEF: Binary Robust Independent Elementary Features. In Proceedings of the European Conference on Computer Vision.         [ Links ]

Calonder, M., Lepetit, V., Ozuysal, M., Trzcinski, T., Strecha, C., & Fua, P. (2012). BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Trans. Pattern Analysis and Machine Intelligence, 34.         [ Links ]

Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detect. Computer Vision and Pattern Recognition, 1, 886-893.         [ Links ]

Fischler, M.A., & Bolles, R.C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24, 381-395.         [ Links ]

Ke, Y., & Sukthankar, R. (2004). Pca-Sift: a more distinctive representation for local image descriptors. Computer Vision and Pattern Recognition, 506-513).         [ Links ]

Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60, 91-110.         [ Links ]

Leutenegger, S., Chli, M., & Siegwart, R.Y. (2011). BRISK: Binary Robust Invariant Scalable Keypoints. In IEEE International Conference on Computer Vision (pp. 2548-2555).         [ Links ]

Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. In IEEE Trans. Pattern Analysis and Machine Intelligence (pp. 1615-1630).         [ Links ]

Rublee, E., & Rabaud, V., Konolige, K., & Bradski, G. (2006). ORB: an efficient alternative to SIFT or SURF. In European Conference on Computer Vision (pp. 404-417).         [ Links ]

Rosten, E., Porter, R., & Drummond, T. (2010). Faster and better: a machine learning approach to corner detect. IEEE Trans. Pattern Analysis and Machine Intelligence, 32, 105-119.         [ Links ]

Rosten, E., & Drummond, T. (2006). Machine learning for highspeed corner detect. In European Conference on Computer Vision (vol. 1).         [ Links ]

Tuytelaars, T., & Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 3, 177-280.         [ Links ]

Tran, Q.H., Chin, T.J., Carneiro, G., Brown, M.S., & Suter, D. (2012). In defence of RANSAC for Outlier Rejection in Deformable Registration. In European Conference on Computer Vision (pp. 274-287).         [ Links ]

Yasmin, M., Mohsin, S., & Sharif, M. (2014). Intelligent image retrieval techniques: a survey. Journal of Applied Research and Technology, 12, 87-103.         [ Links ]

Yasmin, M., Sharif, M., Irum, I., & Mohsin, S. (2013). Powerful descriptor for image retrieval based on angle edge and histograms. Journal of Applied Research and Technology, 11, 727-732.         [ Links ]