|
|
|
|
|
|
3D Model Feature Extraction Based on Light Propagation Simulation with Monte Carlo Method |
LIU Hong-hao1, LIU Xian-xi1, ZHANG Kai-xing1,2*, LU Shan1, Lee Heow Pueh3, SONG Zheng-he4 |
1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
2. Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence,Tai’an 271018,China
3. Department of Mechanical Engineering, National University of Singapore, Singapore 117575, Singapore
4. College of Engineering, China Agricultural University, Beijing 100083, China |
|
|
Abstract Three-dimensional model has been showing extensive demand and vitality in modern industrial design, artificial intelligence and software design fields. Traditional feature extraction methods merely depend on model surface feature, which could not sufficiently satisfy complex model feature extraction needs. In order to improve the accuracy of model feature extraction, a 3D model feature extraction method with high discrimination was proposed based on spectral analysis and light propagation attributes. Firstly, the probability of light transmission, scattering and reflection when light propagation in different medium was quantitatively analyzed with scattering coefficient, absorption coefficient and anisotropy parameters. Secondly, the Monte-Carlo method was used to simulate light propagation in complex 3D model, where different feature statistics including angle, distance and energy were obtained to complete feature extraction. Then, the influence factors of photon beam number and constrained space shape were tested for optimal parameters determination. Finally, the feature extraction effectiveness was evaluated on retrieval precision, recall and E-measure. The results showed that the feature extraction accuracy sensitively varied with constrained space shape and the optimal constrained space for photon propagation was sphere; The feature extraction efficiency decreased with more photon beams, and within basic accuracy requirement, 10 000 to 25 000 photon beams were the optimal simulation number; The feature extraction accuracy of proposed method was higher than the wavelet transform, distance-angle and D2 distribution methods, which is more suitable for offline feature extraction of complex 3D models. The proposed simulation method of feature extraction broadens spectral analysis application, which could extract the integrated feature between model surface and internal structure, promoting model feature extraction research.
|
Received: 2019-01-15
Accepted: 2019-05-22
|
|
Corresponding Authors:
ZHANG Kai-xing
E-mail: kaixingzhang@139.com
|
|
[1] Biasotti S, Cerri A, Aono M, et al. Visual Computer, 2016, 32(2): 217.
[2] Li P J, Ma H D, Ming A L. Multimedia Tools and Applications, 2017, 76(7): 10207.
[3] Ji M M, Feng Y F, Xiao J, et al. Neurocomputing, 2015, 169: 23.
[4] Hong Y, Kim J. Ksii Transactions on Internet and Information Systems, 2017, 11(8): 3950.
[5] Wang Q, Yu Y M, Wang T M, et al. Optik, 2016, 127(22): 10539.
[6] YANG Xue, LI Gang, LIU Yan, et al(杨 雪, 李 刚, 刘 妍, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(11): 3476.
[7] Watte R, Aernouts B, Van Beers R, et al. Optics Express, 2015, 23(13): 17467.
[8] Funamizu H, Maeda T, Sasaki S, et al. Opt. Rev., 2014, 21(3): 359.
[9] Lin S F, Wu C C, Hsu C Y, et al. International Journal of Pattern Recognition and Artificial Intelligence, 2011, 25(4): 583.
[10] Modric D, Maretic K P, Hladnik A. Appl. Optics, 2014, 53(33): 7854.
[11] Hajdek K, Miljkovic P, Modric D. Teh. Vjesn., 2014, 21(4): 779.
[12] Periyasamy V, Pramanik M. J. Biomed. Opt., 2014, 19(4): 045003.
[13] Liu Y J, Zheng Y F, Lv L, et al. Visual Computer, 2012, 28(1): 75.
[14] Lian Z H, Godil A, Bustos B, et al. Pattern Recognition, 2013, 46(1): 449.
[15] LIU Hong-hao, ZHAO Xiu-yan, ZHANG Kai-xing, et al(刘洪豪, 赵秀艳, 张开兴, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2018, 49(S1): 338. |
[1] |
FENG Ying-chao1, HUANG Yi-ming2*, LIU Jin-ping1, JIA Chen-peng2, CHEN Peng1, WU Shao-jie2*, REN Xu-kai3, YU Huan-wei3. On-Line Monitoring of Laser Wire Filling Welding Process Based on Emission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1927-1935. |
[2] |
FENG Xin1, 2, FANG Chao1*, GONG Hai-feng2, LOU Xi-cheng1, PENG Ye1. Infrared and Visible Image Fusion Based on Two-Scale Decomposition and
Saliency Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 590-596. |
[3] |
WANG Zhi-xin, WANG Hui-hui, ZHANG Wen-bo, WANG Zhong, LI Yue-e*. Classification and Recognition of Lilies Based on Raman Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 183-189. |
[4] |
CAI Yu1, 2, ZHAO Zhi-fang3, GUO Lian-bo4, CHEN Yun-zhong1, 2*, JIANG Qiong4, LIU Si-min1, 2, ZHANG Cong-zi4, KOU Wei-ping5, HU Xiu-juan5, DENG Fan6, HUANG Wei-hua7. Research on Origin Traceability of Rhizoma Dioscoreae Based on LIBS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 138-144. |
[5] |
YAN Wen-hao1, YANG Xiao-ying1, GENG Xin1, WANG Le-shan1, LÜ Liang1, TIAN Ye1*, LI Ying1, LIN Hong2. Rapid Identification of Fish Products Using Handheld Laser Induced Breakdown Spectroscopy Combined With Random Forest[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3714-3718. |
[6] |
DUAN Hong-wei1, 2, GUO Mei3, ZHU Rong-guang3, NIU Qi-jian1, 2. LIBS Quantitative Analysis of Calorific Value of Straw Charcoal Based on XY Bivariate Feature Extraction Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3435-3440. |
[7] |
YUAN Zhuang1, DONG Da-ming2*. Near-Infrared Spectroscopy Measurement of Contrastive Variational Autoencoder and Its Application in the Detection of Liquid Sample[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3637-3641. |
[8] |
FAN Yuan-chao, CHEN Xiao-jing*, HUANG Guang-zao, YUAN Lei-ming, SHI Wen, CHEN Xi. Evaluation of Aging State of Wire Insulation Materials Based on
Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3161-3167. |
[9] |
YANG Jie-kai1, GUO Zhi-qiang1, HUANG Yuan2, 3*, GAO Hong-sheng1, JIN Ke1, WU Xiang-shuai2, YANG Jie1. Early Classification and Detection of Melon Graft Healing State Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2218-2224. |
[10] |
CHEN Yan-ling, CHENG Liang-lun*, WU Heng*, XU Li-min, HE Wei-jian, LI Feng. A Method of Terahertz Spectrum Material Identification Based on Wavelet Coefficient Graph[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3665-3670. |
[11] |
ZHANG Hui-jie, CAI Chong*, CUI Xu-hong, ZHANG Lei-lei. Rapid Detection of Anthocyanin in Mulberry Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3771-3775. |
[12] |
KONG De-ming1, CHEN Hong-jie1, CHEN Xiao-yu2*, DONG Rui1, WANG Shu-tao1. Research on Oil Identification Method Based on Three-Dimensional Fluorescence Spectroscopy Combined With Sparse Principal Component Analysis and Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3474-3479. |
[13] |
LI Hao-guang1, 2, YU Yun-hua1, 2, PANG Yan1, SHEN Xue-feng1, 2. Research of Parameter Optimization of Preprocessing and Feature Extraction for NIRS Qualitative Analysis Based on PSO Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2742-2747. |
[14] |
CHEN Qi1,3, PAN Tian-hong2,4*, LI Yu-qiang4, LIN Hong4. Geographical Origin Discrimination of Taiping Houkui Tea Using Convolutional Neural Network and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2776-2781. |
[15] |
FENG Chun1, 2, 3, ZHAO Nan-jing1, 3*, YIN Gao-fang1, 3*, GAN Ting-ting1, 3, CHEN Xiao-wei1, 2, 3, CHEN Min1, 2, 3, HUA Hui1, 2, 3, DUAN Jing-bo1, 3, LIU Jian-guo1, 3. Study on Multi-Wavelength Transmission Spectral Feature Extraction Combined With Support Vector Machine for Bacteria Identification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2940-2944. |
|
|
|
|