請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8462
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 徐百輝(Pai-Hui Hsu) | |
dc.contributor.author | Zong-Yi Zhuang | en |
dc.contributor.author | 莊宗易 | zh_TW |
dc.date.accessioned | 2021-05-20T00:55:01Z | - |
dc.date.available | 2025-07-09 | |
dc.date.available | 2021-05-20T00:55:01Z | - |
dc.date.copyright | 2020-07-17 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-07-09 | |
dc.identifier.citation | Aggarwal, C.C., 2018. Neural Networks and Deep Learning. Springer, Cham. Ahmed, E., A. Saint, A.E.R. Shabayek, K. Cherenkova, R. Das, G. Gusev, D. Aouada, and B.E. Ottersten, 2018. Deep Learning Advances on Different 3D Data Representations: A Survey, arXiv: 1808.01462. Behley, J., M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, 2019. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences, arXiv: 1904.01416. Ben Shabat, I., 2017. 3D Point Cloud Classification using Deep Learning-Recent Works, URL:http://www.itzikbs.com/3d-point-cloud-classification-using-deep-learning (last date access: APR 10 2020). Bentley, J.L., 1975. Multidimensional binary search trees used for associative searching, Communications of the ACM, 18(9):509-517. Berger, C., 2014. From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture and Evaluation. Journal of Software Engineering for Robotics,Vol. 5, pp. 63-79. Biosca, J. M. and J. L. Lerma, 2008. Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods, ISPRS Journal of Photogrammetry and Remote Sensing, 63(1):84-98. Boulch, A., B.L. Saux, and N. Audebert, 2017. Unstructured point cloud semantic labeling using deep segmentation networks, In Eurographics Workshop on 3D Object Retrieval, Vol. 2. Breiman, L., 2001. Random forests. Machine Learning, 45(1): 5-32 Bronstein, M., J. Bruna, Y. Lecun, A. Szlam, and P. Vandergheynst, 2017. Geometric Deep Learning: Going beyond Euclidean data, IEEE Signal Processing Magazine, 34(4):18-42. Carlberg, M., P. Gao, G. Chen, and A. Zakhor, 2009. Classifying urban landscape in aerial lidar using 3d shape analysis, IEEE International Conference on Image Processing, pp. 1701-1704. Chehata, N., L. Guo, and C. Mallet, 2009. Airborne lidar feature selection for urban classification using random forests, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, pp. 207-212. Cheng, G., P. Zhou, and J. Han, 2016. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, pp. 7405-7415. Defferrard, M., X. Bresson, and P. Vandergheynst, 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems, pp. 3844-3852. Demantké, J., C. Mallet, N. David, and B. Vallet, 2011. Dimensionality Based Scale Selection in 3d LIDAR Point Clouds, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, pp. 97-102 Dowman, I., 2004. Integration of LiDAR and IFSAR for mapping, International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.35, Part B2, Istanbul, Turkey. Filin, S., 2002. Surface clustering from airborne laser scanning data, International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, Vol. 34, pp. 119-124. Fischler, M.A., and R.C. Bolles, 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM , 24(6):381-395. Gehrung, J., M. Hebel, M. Arens, and U. Stilla, 2017. An Approach To Extract Moving Object From MLS Data Using A Volumetric Background Representation, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.4, pp. 107-114 Geiger, A., P. Lenz, and R. Urtasun, 2012. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, pp. 3354-3361. Geiger, A., P. Lenz, C. Stiller, and R. Urtasun, 2013. Vision meets robotics: The kitti dataset, The International Journal of Robotics Research, 32(11):1231-1237. Griffiths, D., and J. Boehm, 2019a. A Review on Deep Learning Techniques for 3D Sensed Data Classification. Remote Sensing, 11(12):1499. Griffiths, D., and J. Boehm, 2019b. Weighted Point Cloud Augmentation for Neural Network Training Data Class-Imbalance, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42, pp. 981–987. Grilli, E., F. Menna, and F. Remondino, 2017. A review of point clouds segmentation and classification algorithms. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42, pp.339-344. Guinard S., and L. Landrieu, 2017. Weakly Supervised Segmented-Aided Classification of Urban Scenes From 3D LIDAR Point Clouds, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.42, pp. 151-157 Hough, P.V.C, 1962. Method and means for recognizing complex patterns. US Patent, no. 3069654. Hunter, G.M., 1978. Efficient computation and data structures for graphics. Ph.D. dissertation, Princeton University, USA. Hu, Q., B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, 2020. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Kowalczuk, Z., and K. Szymański, 2019. Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model, IFAC-PapersOnLine, 52(8):416-421. Krizhevsky, A., I. Sutskever, and G.E. Hinton, 2012. ImageNet classification with deep convolutional neural networks, Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol. 1, Lake Tahoe, Nevada, pp. 1097-1105. Kubat, M., 2015. An Introduction to Machine Learning. Springer, Cham. LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning, Nature, 521:436-444. Lin, M., Q. Chen, and S. Yan, 2014. Network In Network, International Conference on Learning Representations , Banff, Canada. Li, R., X. Li, P.A. Heng, and C.W. Fu, 2020. Pointaugment: an auto-augmentation framework for point cloud classification, arXiv: 2002.10876. Long, Y., Gong, Y., Xiao, Z.h., Liu, Q., 2017. Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, pp. 2486-2498. Malinverni, E., R. Pierdicca, M. Paolanti, M. Martini, C. Morbidoni, F. Matrone, and A. Lingua, 2019. Deep Learning for Semantic Segmentation of 3d Point Cloud, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42, pp. 735-742. Maturana, D., and S. Scherer, 2015. VoxNet: A 3D Convolutional Neural Network for real-time object recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, pp. 922-928. Munoz, D., J.A. Bagnell, N. Vandapel, and M. Hebert, 2009. Contextual Classification with Functional Max-Margin Markov Networks, IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, pp. 975-982. Nguyen A., and B. Le, 2013. 3d point cloud segmentation: A survey, IEEE Conference on Robotics, Automation and Mechatronics, Manila, pp. 225-230, RAM Niemeyer, J., F. Rottensteiner, and U. Soergel, 2012. Conditional random fields for lidar point cloud classification in complex urban areas, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 3, pp. 263-268. Niemeyer, J., F. Rottensteiner, and U. Soergel, 2014. Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 87, pp.152-165. PDOK, https://www.pdok.nl/nl/ahn3-downloads (last date access: 10 MAR 2019). Pingel, Thomas J., Keith C. Clarke, and William A. McBride, 2013. An Improved Simple Morphological Filter for the Terrain Classification of Airborne LIDAR Data, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 77, pp. 21-30. Point Cloud Library, 2020. Documentation for Module Search, URL: http://www.pointclouds.org/documentation/ (last date accessed: 2 April 2020). Qi, C.R., H. Su, M. Nießner, A. Dai, M. Yan, and L.J. Guibas, 2016. Volumetric and Multi-view CNNs for Object Classification on 3D Data, IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, pp. 5648-5656. Qi, C.R., H. Su, M. Kaichun, and L.J. Guibas, 2017a. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 77-85. Qi, C.R., L. Yi, H. Su, and L.J. Guibas, 2017b. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, Advances in Neural Information Processing Systems, Long Beach, California, pp. 5099-5108. Quadros, A., J.P. Underwood, and B. Douillard, 2012. An Occlusion-Aware Feature for Range Images. IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, pp. 4428-4435. Rabbani, T., F. Van Den Heuvel, and G. Vosselmann, 2006. Segmentation of point clouds using smoothness constraint, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, pp. 248-253. Rawat, W., and Z. Wang, 2017. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review, Neural Computation, 29(9):1-98. Rottensteiner, F., G. Sohn, M. Gerke, and J. D. Wegner, 2013. ISPRS Test Project on Urban Classification and 3D Building Reconstruction Commission III-Photogrammetric Computer Vision and Image Analysis, Working Group III/4-3D Scene Analysis, pp.1-17. Roynard, X.A., J.E. Deschaud, and F. Goulette, 2018. Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification. International Journal of Robotics Research, 37(6):545–557 Ruan, X., and, B. Liu, 2020. Review of 3D Point Cloud Data Segmentation Methods. International Journal of Advanced Network, Monitoring and Controls. Vol. 5, pp.66-71. Sampath, A., and J. Shan, 2010. Segmentation and reconstruction of polyhedral building roofs from aerial lidar point clouds, IEEE Transactions on geoscience and remote sensing, 48(3):1554-1567. Sarkar, D., R. Bali, and T. Sharma, 2018. Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems, Apress, Apress, Berkeley, CA. Sermanet, P., K. Kavukcuoglu, S. Chintala, and Y. LeCun, 2013. Pedestrian Detection with Unsupervised Multi-stage Feature Learning, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626-3633. Shahzad, M., and X. X. Zhu, 2015. Robust reconstruction of building facades for large areas using spaceborne tomosar point clouds, IEEE Transactions on Geoscience and Remote Sensing, 53(2):752-769. Simonovsky, M., and N. Komodakis, 2017. Dynamic edgeconditioned filters in convolutional neural networks on graphs. IEEE Conference on Computer Vision and Pattern Recognition, pp. 3693-3702. Soilán , M., R. Lindenbergh, and B. Riveiro, A. Sánchez-Rodríguez, 2019. Pointnet For the Automatic Classification of Aerial Point Clouds, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 4, pp. 445-452 Su, H., S. Maji, E. Kalogerakis, and E. Learned-Miller, 2015. Multi-view convolutional neural networks for 3d shape recognition, IEEE International Conference on Computer Vision, pp. 945-953. Thomas, H., C.R. Qi, J.E. Deschaud, B. Marcotegui, F. Goulette, and L.J. Guibas, 2019. KPConv: Flexible and Deformable Convolution for Point Clouds, arXiv: 1904.08889 Vallet, B., M. Brédif, A. Serna, B. Marcotegui, and N. Paparoditis, 2015. TerraMobilita/iQmulus Urban Point Cloud Analysis Benchmark. Computers Graphics, Vol. 49, pp. 126–133. Wang, H., and M. Ren, 2011. Lane Markers Detection based on Consecutive Threshold Segmentation, Journal of Information and Computing Science, Vol. 6, pp. 207-212. Wang, H., Z. Cai, H. Luo, C. Wang, P. Li, W. Yang, S. Ren, and J. Li, 2012. Automatic road extraction from mobile laser scanning data, International Conference on Computer Vision in Remote Sensing, Xiamen, pp. 136-139. Wang, Y., Y. Sun, Z. Liu, S.E. Sarma, M.M. Bronstein, and J.M. Solomon, 2019. Dynamic graph cnn for learning on point clouds, ACM Transactions on Graphics (TOG). Weinmann, M., A. Schmidt, C. Mallet, S. Hinz, F. Rottensteiner, and B. Jutzi, 2015a. Contextual classification of point cloud data by exploiting individual 3d neigbourhoods, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 2, pp. 271-278. Weinmann, M., B. Jutzi, S. Hinz, and C. Mallet, 2015b. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 105, pp. 286-304. Wichmann, A., A. Agoub, and M. Kada, 2018. RoofN3D: Deep Learning Training Data for 3D Building Reconstruction, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 42, pp.1191-1198. Winiwarter, L., Mandlburger, G., 2019. Classification of 3D Point Clouds using Deep Neural Networks, Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V., Band 28, pp.663-674. Wu, Z., S. Song, A. Khosia, F. Yu, L. Zhang, X. Tang, and J. Xiao, 2015. 3D ShapeNets: A deep representation for volumetric shapes, IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, pp. 1912-1920. Xie, Y., J. Tian, and X.X. Zhu, 2019. A Review of Point Cloud Semantic Segmentation, arXiv:1908.08854 Xu, Y., W. Yao, S. Tuttas, L. Hoegner, and U. Stilla, 2018. Unsupervised segmentation of point clouds from buildings using hierarchical clustering based on gestalt principles, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 99, pp. 1-17. Zhang, J., X. Lin, and X. Ning, 2013. Svm-based classification of segmented airborne lidar point clouds in urban areas, Remote Sensing, 5(8):3749–3775. Zolanvari, I., S. Ruano, A. Rana, A. Cummins, R. Da Silva, M. Rahbar, and A. Smolic, 2019. DublinCity: Annotated LiDAR Point Cloud and its Applications, arXiv:1909.03613 王淼、湯凱佩、曾義星,2005,光達資料八分樹結構化於平面特徵萃取,航測及遙測學刊, 10(1):59-70。 林耿帆,2012。以物件為基礎之光達點雲分類。碩士論文,國立臺灣大學土木工程學研究所,台北。 林耿帆、徐百輝,2014。以物件為基礎之光達點雲分類,航測及遙測學刊,19(1):19-41。 賴泓瑞、陳俊元、林昭宏,2010。以模型樣版為基礎之建物三維點雲建模演算法,航測及遙測學刊,15(2):189-199。 羅英哲、曾義星,2009。光達點雲資料面特徵重建,航測及遙測學刊,14(3):171-184。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8462 | - |
dc.description.abstract | 為提升光達點雲分類之效能及準確性,本研究利用三個表現良好的深度學習模型PointNet、PointNet++及KPConv於自行標註的光達資料進行點雲語義分類的實驗。在分類程序上,不同於過去需先分類出地面/非地面點的處理模式,也不需由人工設計特徵以分類器進行點雲分類,而是由模型自動進行特徵的萃取及分類。此外,本研究透過分析資料的正規化方式及加入幾何特徵的資料組合,進行模型最佳化參數的評估及調整,找出一個最佳的策略以利後續應用。 為驗證不同輸入特徵組合的深度學習模型於各式光達資料之適用性,實驗中分別以ALS及MLS資料進行自動化地物分類,除了以各項準確度指標評估及可視化分析外,同時也與傳統機器學習演算法隨機森林與商用軟體LiDAR360的分類成果進作比較。實驗成果顯示,在兩類型的資料中,三維深度學習的方法皆優於傳統方法及商用軟體。其中幾何特徵及強度資訊的加入對PointNet的分類成效有顯著的提升,對採用階層式架構以鄰域球萃取局部特徵的PointNet++也有些許幫助;KPConv則透過點卷積的方式於原始點雲坐標學習到更具細節的局部特徵。而對於各模型來說,不管是對ALS還是MLS的場景應用,高程扮演著一個重要的特徵,尤其在ALS場景中更是明顯。最終由KPConv分別在ALS及MLS資料中獲得最佳的分類成果,但其模型較複雜且計算時間長;PointNet++則在準確度與計算效率上都在有不錯的表現;而PointNet透過加入幾何特徵,達到不亞於PointNet++的表現,且其模型更簡單、計算更快速。 | zh_TW |
dc.description.abstract | To improve the efficiency and accuracy of LiDAR point cloud classification, three crucial deep learning models, PointNet, PointNet++ and KPConv are utilized in this study to experiment on semantic classification of point cloud for LiDAR data labeled manually. In the classification procedure, the processing mode is different from the past that needs to classify ground/non-ground points first, or design features manually to classify the point cloud. However, our model can extract and classify the features automatically. In addition, the research finds an optimal strategy for subsequent application, through the analysis of the data normalization, the combination of additional geometric features, and the model parameters optimization and adjustment. In order to verify the applicability of the used deep learning model with different input feature combinations to various types of LiDAR data, airborne laser scanning and mobile laser scanning data sets were applied to automatic land feature classification in this study. Besides the classification indicators evaluation and visual analysis, the classification results were compared with regular machine learning algorithms, random forest and commercial software, LiDAR360. The results show that in the two types of data, the result of 3D deep learning are superior to traditional methods and commercial software, in addition, the addition of geometric features and intensity information has significantly improved the classification results of PointNet, and it is also a little help for PointNet++ that uses a hierarchical structure to extract local features with neighborhood balls; KPConv uses point convolution to learns more detailed local features from the original point cloud coordinates. In addition, for each model, elevation plays an important role, especially in ALS data. Finally, KPConv obtained the best classification results in both ALS and MLS data, but it is more complicated and time consuming; PointNet++ has a good performance both in accuracy and efficiency; and PointNet achieves performance comparable to PointNet++ by adding geometric features, and it is simpler and more efficient. Different models and strategies can be adopted according to different application levels. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T00:55:01Z (GMT). No. of bitstreams: 1 U0001-0607202009292100.pdf: 9659008 bytes, checksum: 803b0d7e0565928baa85aa35edfbb07e (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 論文口試委員審定書 i 致謝 ii 中文摘要 iii 英文摘要 iv 目錄 v 圖目錄 viii 表目錄 xii 第一章、緒論 1 1.1 前言 1 1.2 研究動機與目的 3 1.3 研究流程 5 1.4 論文架構 7 第二章、文獻回顧 8 2.1 點雲資料的分類及分割 8 2.1.1 傳統點雲分割 9 2.1.2 點雲語義分割 11 2.2 二維深度學習的發展及在影像上的應用 13 2.3 三維深度學習的發展與應用 15 2.4 直接應用深度學習於點雲資料之挑戰 16 2.5 大型室外場景之光達點雲資料集 19 第三章、研究方法 23 3.1 點雲特徵計算 23 3.2 隨機森林 27 3.3 卷積神經網路 28 3.4 適用於三維點雲資料處理的卷積神經網路 32 3.4.1 PointNet 32 3.4.2 PointNet++ 36 3.4.3 KPConv 40 3.5 空載光達點雲之語義分類模型 44 3.5.1 PointNet於成大空載光達資料 45 3.5.2 PointNet++於成大空載光達資料 46 3.5.3 KPConv於成大空載光達資料 47 3.6 車載光達點雲之語義分類 47 3.6.1 PointNet於沙崙車載光達資料 48 3.6.2 PointNet++於沙崙車載光達資料 49 3.6.3 KPConv於沙崙車載光達資料 50 3.7 資料增強 50 3.8 點雲語義分類品質評估 51 第四章、研究資料 53 4.1 光達資料介紹 53 4.2 本研究實驗區域 54 4.3 點雲標註 56 4.4 實驗資料前處理 60 4.4.1 點雲結構化 60 4.4.2 點雲資料降採樣 62 4.4.3 點雲切塊 63 第五章、實驗與成果分析 69 5.1 空載光達點雲資料語義分類成果 69 5.1.1 PointNet在成大空載光達資料中的分類成果 69 5.1.2 PointNet++在成大空載光達資料中的分類成果 78 5.1.3 KPConv在成大空載光達資料中的分類成果 85 5.1.4 與隨機森林和商用軟體分類成果比較分析(成大ALS資料) 86 5.2 車載光達點雲資料語義分類成果 90 5.2.1 PointNet在沙崙車載光達資料中的分類成果 90 5.2.2 PointNet++在沙崙車載光達資料中的分類成果 94 5.2.3 KPConv在沙崙車載光達資料中的分類成果 97 5.2.4 與隨機森林和商用軟體分類成果比較分析(沙崙MLS資料) 99 第六章、結論與建議 104 參考文獻 107 | |
dc.language.iso | zh-TW | |
dc.title | 應用三維深度學習於光達點雲資料之語義分類 | zh_TW |
dc.title | Semantic Classification of LiDAR Point Cloud with 3D Deep Learning | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 曾義星(Yi-Hsing Tseng),邱式鴻(Shih-Hong Chio),趙鍵哲(Jen-Jer Jaw) | |
dc.subject.keyword | 光達,點雲資料,三維深度學習,語義分類, | zh_TW |
dc.subject.keyword | LiDAR,point cloud,3D deep learning,semantic classification, | en |
dc.relation.page | 115 | |
dc.identifier.doi | 10.6342/NTU202001331 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2020-07-09 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
dc.date.embargo-lift | 2025-07-09 | - |
顯示於系所單位: | 土木工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0607202009292100.pdf 此日期後於網路公開 2025-07-09 | 9.43 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。