2020 Vol.3 Apr. No.2

• Title: Reconstruction method of non rigid 3D CT mechanical scanning image features
• Name: Ma Xiaoli
• Company: Anhui Jianzhu University
• Abstract:

  In the reconstruction of non-rigid three dimensional (3D) point cloud image features, the image data is large, which leads to the difficulty for analysis, and the achieving process is complex. In this paper, the reconstruction method for non-rigid 3D point cloud image features based on NURBS curve is proposed to collect non-rigid 3D point cloud image data, and achieve triangulation. The operation of point cloud data neighborhood search is limited in the local area, and the data of non-rigid 3D point cloud images are divided with the idea of space division. The least square plane is obtained by using preprocessed non-rigid 3D point cloud data, and the non-rigid 3D point cloud data is mapped and converted into a two dimensional (2D) point cloud model. The cumulative chord length parameterization method is adopted to introduce weighting factor for NURBS curve description using the rational polynomial function, based on the definition and properties of NURBS curve, point, line, surface reconstruction model is utilized to reconstruct the non-rigid 3D point cloud image feature. The simulation results show that the proposed method can reconstruct the 3D point cloud images feature with less time and the reconstruction effect is better than the Crust method.

• Keyword: non-rigid; 3D point cloud; image feature; reconstruction
• DOI: 10.12250/jpciams2020020218
• Citation form: Ma Xiaoli.Reconstruction method of non rigid 3D CT mechanical scanning image features[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 35-42.

[1] Jin-Kee Chae;Jae-Yung Lee;Min-Ho Lee;Jong-Ki Han;Nguyen,T.Q;Woon-Young Yeo.Cubic Convolution Scaler Optimized for Local Property of Image Data . In IEEE pages 4796-4809,2015.9,17.
[2] Li, Z. ; Wang, M. ; Zhao, R. Neighborhood Discriminant Hashing for Large-Scale Image Retrieval. In TIP pages 2827-2840,2015.5,25.
[3] Ruimao Zhang;Liang Lin;Rui Zhang;Wangmeng Zuo;Lei Zhang.Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification . In IEEE, pages 4766-4779,2015.3,6
[4] Guiduo Duan ; Fujin Zhong . Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet .In TIP pages 2344-2354,2015.4,30.
[5] Tao Xiang ; Shaogang Gong. Modelling activity global temporal dependencies using Time Delayed Probabilistic Graphical Model. In ICCV, pages 120-127,2009.4,9.
[6] Liu, J. ; Yang, W. ; Guo, Z. Image Super-Resolution Based on Structure-Modulated Sparse Representation .In TIP pages 2797-2810,2015.5,25.
[7] Liangliang Cao ; Fengjun Lv ; Shuicheng Yan. Action detection in complex scenes with spatial and temporal ambiguities. In ICCV, pages 128-135,2009.6,8.
[8] Alabort-i-Medina, J. ; Tzimiropoulos, G. ; Zafeiriou, S.P. Feature-Based Lucas–Kanade and Active Appearance Models. In TIP pages 2617-2632,2015.5,19.
[9] Kav-Venaki, E. ; Peleg, S. Shift-map image editing. In ICCV, pages 151-158,2009.12,8.
[10] Min Sun ; Li Fei-Fei ; Savarese, S. Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories. In ICCV, pages 213-220,2009.11,8.
[11] Jurie, F. ; Schmid, C. Combining efficient object localization and image classification. In ICCV, pages 237-244,2009.8,30.
[12] Chen, L. Image Quality Assessment Using Human Visual DOG Model Fused With Random Forest. In TIP pages 3282-3292,2015.6,23. 
[13] Meng Li; Ghosal,S.Fast Translation Invariant Multiscale Image Denoising . In IEEE, pages 4876-4887,2015.9,21.
[14] Licheng Yu ; Hongteng Xu ; Hao Zhang . Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis .In TIP pages 1315-1329,2015.2,19.
[15] McNeill, G. ; Qian, Y. Storyboard sketches for Content Based Video Retrieval. In ICCV, pages 245-252,2009.10,1.
[16] Mbouna, R.O. Head Pose Estimation From a 2D Face Image Using 3D Face Morphing With Depth Parameters.In TIP pages 1801 – 1808.2015.3,26.
[17] Fujiao Ju;Yanfeng Sun;Junbin Gao;Yongli Hu;Baocai Yin. Image Outlier Detection and Feature Extraction via L1-Norm-Based 2D Probabilistic PCA . In IEEE, pages 4834-4846.2015.9.12.
[18] Kassubeck, M. ; Bauszat, P. ; Eisemann, M. An Approach Toward Fast Gradient-Based Image Segmentation .In TIP pages 2633-2645,2015.5,22.
[19] Lempitsky, V. ; Kohli, P. ; Rother, C. ; Sharp, T. Image segmentation with a bounding box prior. In ICCV, pages 277-284,2009.6,18.
[20] Xiaoyan Sun ; Jingyu Yang ; Feng Wu. Image Denoising by Exploring External and Internal Correlations. In TIP pages 1967-1982,2015.3,26.
[21] Hu,S; Jin,L;Wang,H;Zhang,Y. Compressed Image Quality Metric Based on Perceptually Weighted Distortion. In IEEE, pages 5594-5608,2015.10.11.
[22] Huiling Zhou ; Kin-Man Lam . High-resolution face verification using pore-scale facial features .In TIP pages 2317-2327,2015.4,14.
[23] Yi Xu;Licheng Yu;Hongteng Xu;Hao Zhang.Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis. In IEEE, pages 1315- 1329,2015.2,19.
[24] S Liu, X Cheng*, W Fu, et al. Numeric characteristics of generalized M-set with its asymptote [J]. Applied Mathematics and Computation, 2014, 243:767-774, SCI.
[25] Halimi,A;Dobigeon,N.;Toumeret,J.-Y.Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability . In IEEE, pages 4904-4917,2015.8,21.