基於Kinect的手勢追踪來開發Wayang Kulit劇院

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：21

、訪客IP：3.145.152.49

姓名

歐文尼斯(Ervin Yohannes) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於Kinect的手勢追踪來開發Wayang Kulit劇院
(KINECT-BASED HAND GESTURE TRACKING FOR DEVELOPING WAYANG KULIT THEATER)

相關論文

★ 基於edX線上討論板社交關係之分組機制	★ 利用Kinect建置3D視覺化之Facebook互動系統
★ 利用 Kinect建置智慧型教室之評量系統	★ 基於行動裝置應用之智慧型都會區路徑規劃機制
★ 基於分析關鍵動量相關性之動態紋理轉換	★ 基於保護影像中直線結構的細縫裁減系統
★ 建基於開放式網路社群學習環境之社群推薦機制	★ 英語作為外語的互動式情境學習環境之系統設計
★ 基於膚色保存之情感色彩轉換機制	★ 一個用於虛擬鍵盤之手勢識別框架
★ 分數冪次型灰色生成預測模型誤差分析暨電腦工具箱之研發	★ 使用慣性傳感器構建即時人體骨架動作
★ 基於多台攝影機即時三維建模	★ 基於互補度與社群網路分析於基因演算法之分組機制
★ 即時手部追蹤之虛擬樂器演奏系統	★ 基於類神經網路之即時虛擬樂器演奏系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

在印度尼西亞有許多傳統的遊戲和劇院，這些遊戲跟劇院適合所有年齡層的人們，最有名就是皮影戲，它是用戲偶的影子動作來進行表演，能夠表演不同類型的劇本，像是喜劇、歷史、愛情等等。其中在印度尼西亞受大家歡迎的就是愛情劇，叫做Ramayana的故事，在這個故事中有五個角色，包含Anoman, Sinta, Rama, Laksma, 和Rahwana。Rahwana是屬於反派的一方，其他人是正派的一方。我們的皮影戲利用手勢辯來操控建構出來的虛擬角色，引入叫做blender的工具來創造2D或3D的物件，選擇在Unity的環境中來進行開發，Unity能夠結合Kinect的相關模組進行更多有趣的變化。首先，因為需要兩隻手來操控戲偶，需要將手勢進行左右手的分群來進行辯識，主要使用的方法為DBSCAN分群法，能夠自動且有效率的分群，在DBSCAN的方法中Eps和MinPts是對於能否得到好的結果重要的兩個參數。右手和左手的群集會先搜尋各自的平均數，這個過程也可以叫做標記，因為系統會選擇哪一個為平均數1和平均數2。找完平均數後，就能開始追蹤手部動作，平均數1和平均數2之間的距離我們設置了三種情況能夠觸發不同效果，第一種距離就是當其他皮影戲偶遇到Rahwana時，如果距離在50~100之間就會觸發打鬥，第二種距離就是當距離為100~200之間，會撥放動畫在每個皮影戲偶上，最後一種就是當距離超過200時，就讓每個皮影戲偶正常表演。我們利用模板匹配的方法，設定不同手部方向的模板來進行手勢辯識，每個方向都能夠對皮影戲偶產生不同的效果。本研究的貢獻為評估DBSCAN門檻值、手勢辯識和皮影戲表演的使用者體驗。DBSCAN門檻值在25時對於我們系統手勢辯識的部分是最好的，使用者體驗評估的部分包含了6項東西，吸引力、效能表現、簡明性、可靠性、刺激性和新奇程度，我們觀察了20個人，他們對於皮影戲有不同等級的熟悉程度，根據觀察後的結果，以上因素都表現良好。我們的系統未來能夠改善的東西包含了: 使用其他的辯識方法、變更故事內容、改變皮影戲偶的表演方式等等。

摘要(英)

In Indonesia, there are many traditional games and theater. Both games and theater can be playing by all person from a kid until mature. The popular theater is playing doll and shadow together by person and It is called Wayang Kulit theater. Many storytellings in Wayang Kulit from comedy, history, romance and other. Romance story is popular story than another story which is Ramayana story. In this story, there is five character including Anoman, Sinta, Rama, Laksma, and Rahwana. In the Wayang Kulit theater usually there are Gunungan to give sign background or storytelling changed. Rahwana is a bad character and others are a good character. The Wayang Kulit theater using hand gesture recognition for playing. The 3D object made from Blender which is blender is a tool for designing 2D or 3D object, many features inside for designing or modeling, give the texture of object and etc. Blender is free than another tool for the same purpose and it is user-friendly so user can be understanding the program or tool quickly. The programming using C# by related Unity. Unity is game development tool and many great games made from Unity because of so many features, the tool so interactive and the important thing it can be relating to Kinect. Firstly, in theater need two hands to generate or move Wayang Kulit so need clustering method for cluster right and left hand. The clustering method using DBSCAN clustering because can be cluster automatically and efficiently than another method. There are 2 important variables in DBSCAN including Eps and MinPts which are need validate threshold to get good result of the cluster. The right and left hand will search about each median for next processing. The search median can be namely labeling processing because the system will be choosing which one median1 and median2. After finding each median the processing can recognize about gesture movement. The gesture is choosing the Wayang Kulit in above there are six objects. After that, the distance between median1 and median2 will be compared. There is three distance in this performance. The first distance is fighting effect which is distance range is 50 to 100 that occur if Wayang Kulit meets the Rahwana so show the fighting ability. The second distance is playing the animation in each Wayang Kulit and the distance is 100 to 200. The last is important gesture because each Wayang Kulit can show each ability and the distance range more than 200. The last, gesture recognition using template matching method which is there are three templates including down, right, left direction. Each direction will be shown different ability in each Wayang Kulit but only two Wayang Kulit has three abilities and the other just has one ability. The evaluation of this research consists of DBSCAN threshold, gesture recognition, and UI / UX evaluation. The DBSCAN threshold evaluation looks for the best threshold for clustering right and left hand. From the result, the best threshold is 25 because it can be clustered median1 and median2 exactly. For the gesture recognition evaluation, we will observe 20 people from various knowledge discipline for playing Wayang Kulit performance and the gesture recognition focus in the last threshold that distance range more than 200. The average of all result above 90% and the best result is median 2 on gesture 1 which is result 100%. The last UI / UX evaluation which is 20 people of hand gesture recognition will get a questionnaire and ask to fill it after playing Wayang Kulit performance. The UI / UX must contain 6 factors including attractiveness, efficiency, perspicuity, dependability, stimulation, and novelty. From the evaluation result overall is good for our system. The only improvement in the future the result is very good it means our system can be interested people to developing our system in the future through using another method, change of story, change the Wayang Kulit performance, and others.

關鍵字(中)

★ 皮影戲
★ 模板匹配
★ 手勢辯識
★ 深度傳感器

關鍵字(英)

★ Wayang Kulit
★ template matching
★ hand gesture recognition
★ depth sensor

論文目次

摘要 i
ABSTRACT iii
Acknowledgements v
Table of Content vi
List of Figures viii
List of Tables ix
List of Algorithm x
Chapter I Introduction 1
1.1 Background 1
1.2 Problem Definition 2
1.3 Scope and Limitation 3
1.4 Thesis Overview 3
Chapter II Literature Review 4
2.1 Wayang Kulit 4
2.2 Tools 5
2.2.1 Blender 5
2.2.2 Unity 6
2.2.3 Kinect version 2 7
2.3 Depth Sensor 9
2.4 DBSCAN 10
2.5 Hand Gesture Recognition 11
2.6 Template Matching 13
2.7 User Experience 14
Chapter III Research Methodology and Design 15
3.1 Literature Study 15
3.2 Technical Requirement 15
3.3 Data Acquisition 16
3.3.1 3D Model 16
3.3.2 Particle System 19
3.3.3 Depth Data Description 19
3.3.4 Sounds 21
3.4 System Design 21
3.4.1 Clustering 21
3.4.2 Tracking and Labelling 24
3.4.3 Hand Gesture Recognition 26
3.5 Interface Design 29
3.6 Evaluation Design 30
3.7 Scenario and Wayang Kulit Story Design 32
Chapter IV System Implementation 35
4.1 System Architecture 35
4.2 System Implementation 36
4.2.1 3D Model and Particle System 36
4.2.2 Algorithms of Implementation 39
4.3 Interface Implementation 43
Chapter V Evaluation and Discussion 47
5.1 Evaluation 47
5.1.1 Threshold value in DBSCAN 47
5.1.2 Hand Gesture Recognition 50
5.1.3 User Experience (UX) / User Interface (UI) 53
5.2 Discussion 54
5.2.1 Effect of Threshold 54
5.2.2 Hand Gesture Recognition 56
5.2.3 User Experience (UX) / User Interface (UI) 59
Chapter VI Conclusion and Future Works 62
6.1 Conclusion 62
6.2 Future Works 63
References 64

參考文獻

[1] Ghani, D. A., & Ishak, M. S. B. A. (2011). Preserving Wayang Kulitfor Future Generations. IEEE MultiMedia, 18(4), 70-74.
[2] Ghani, D. B. A. (2012). Seri Rama: Converting a shadow play puppet to street fighter. IEEE computer graphics and applications, 32(1), 8-11.
[3] Yang, L., Zhang, L., Dong, H., Alelaiwi, A., & El Saddik, A. (2015). Evaluating and improving the depth accuracy of Kinect for Windows v2. IEEE Sensors Journal, 15(8), 4275-4285.
[4] Jais, H. M., Mahayuddin, Z. R., & Arshad, H. (2015, August). A review on gesture recognition using Kinect. In Electrical Engineering and Informatics (ICEEI), 2015 International Conference on (pp. 594-599). IEEE.
[5] Aziz, M. A. A., Niu, J., Zhao, X., & Li, X. (2016). Efficient and robust learning for sustainable and reacquisition-enabled hand tracking. IEEE transactions on cybernetics, 46(4), 945-958.
[6] Morariu, V. I., Harwood, D., & Davis, L. S. (2013). Tracking people′s hands and feet using mixed network and/or search. IEEE transactions on pattern analysis and machine intelligence, 35(5), 1248-1262.
[7] Wen, L., Lei, Z., Lyu, S., Li, S. Z., & Yang, M. H. (2016). Exploiting hierarchical dense structures on hypergraphs for multi-object tracking. IEEE transactions on pattern analysis and machine intelligence, 38(10), 1983-1996.
[8] Choi, J., & Maurer, M. (2016). Local volumetric hybrid-map-based simultaneous localization and mapping with moving object tracking. IEEE Transactions on Intelligent Transportation Systems, 17(9), 2440-2455.
[9] Zhang, X., Li, W., Ye, X., & Maybank, S. (2015). Robust hand tracking via novel multi-cue integration. Neurocomputing, 157, 296-305.
[10] Wu, X., Mao, X., Chen, L., Xue, Y., & Rovetta, A. (2015). Depth image-based hand tracking in complex scene. Optik-International Journal for Light and Electron Optics, 126(20), 2757-2763.
[11] Kim, J., Yu, S., Kim, D., Toh, K. A., & Lee, S. (2017). An adaptive local binary pattern for 3d hand tracking. Pattern Recognition, 61, 139-152.
[12] Sun, L., Liu, G., & Liu, Y. (2014). 3D hand tracking with head mounted gaze-directed camera. IEEE Sensors Journal, 14(5), 1380-1390.
[13] Kusumanugraha, S., Ito, A., Mikami, K., & Kondo, K. (2011, October). An Analysis of Indonesian Traditional” Wayang Kulit Kulit” Puppet 3D Shapes Based on Their Roles in the Story. In Culture and Computing (Culture Computing), 2011 Second International Conference on (pp. 147-148). IEEE.
[14] Tomo, T. P., Enriquez, G., & Hashimoto, S. (2015, December). Indonesian puppet theater robot with gamelan music emotion recognition. In Robotics and Biomimetics (ROBIO), 2015 IEEE International Conference on (pp. 1177-1182). IEEE.
[15] Kia, K. K., & Chan, Y. M. (2009, August). A study on the visual styles of Wayang KulitKelantan and its capturing methods. In Computer Graphics, Imaging and Visualization, 2009. CGIV′09. Sixth International Conference on(pp. 423-428). IEEE.
[16] Ahmad, J., & Jamaludin, Z. (2014, September). Embedding interaction design in Wayang Kulitmathematics courseware. In User Science and Engineering (i-USEr), 2014 3rd International Conference on (pp. 7-12). IEEE.
[17] Ghani, D. A. (2011, April). Wayang Kulit kulit: Digital puppetry character rigging using Maya MEL language. In Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on (pp. 1-5). IEEE.
[18] Grahita, B., Komma, T., & Kushiyama, K. (2013, September). CG Programming Approach to Generate Pattern of Wayang Kulit Beber Pacitan Character′s Cloth. In Culture and Computing (Culture Computing), 2013 International Conference on (pp. 183-184). IEEE.
[19] Bhawar, P., Ayer, N., & Sahasrabudhe, S. (2013, December). Methodology to create optimized 3d models using blender for android devices. In Technology for Education (T4E), 2013 IEEE Fifth International Conference on (pp. 139-142). IEEE.
[20] Baglivo, A., Ponti, F. D., De Luca, D., Guidazzoli, A., Liguori, M. C., & Fanini, B. (2013, October). X3D/X3DOM, Blender Game Engine and OSG4WEB: open source visualisation for cultural heritage environments. In Digital Heritage International Congress (DigitalHeritage), 2013 (Vol. 2, pp. 711-718). IEEE.
[21] Dere, S., Sahasrabudhe, S., & Iyer, S. (2010, July). Creating open source repository of 3D models of laboratory equipments using Blender. In Technology for Education (T4E), 2010 International Conference on (pp. 149-156). IEEE.
[22] Starzyk, J. A., & Raif, P. (2013, April). Cognitive agent and its implementation in the blender game engine environment. In Computational Intelligence for Human-like Intelligence (CIHLI), 2013 IEEE Symposium on (pp. 1-8). IEEE.
[23] Kadam, K., & Iyer, S. (2014, July). Improvement of Problem Solving Skills in Engineering Drawing Using Blender Based Mental Rotation Training. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on (pp. 401-402). IEEE.
[24] Haschka, T., Dauchez, M., & Henon, E. (2015, March). Visualization of molecular properties at the qantum mechanical level using blender. In Virtual and Augmented Reality for Molecular Science (VARMS@ IEEEVR), 2015 IEEE 1st International Workshop on (pp. 7-13). IEEE.
[25] Kadam, K., & Iyer, S. (2015, July). Impact of blender based 3d mental rotation ability training on engineering drawing skills. In Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on (pp. 370-374). IEEE.
[26] Jiyuan, L., & Wenfeng, H. (2016, May). Development of puzzle game about children′s etiquette based on Unity3D. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS International Conference on (pp. 495-500). IEEE.
[27] Jie, J., Yang, K., & Haihui, S. (2011, October). Research on the 3D game scene optimization of mobile phone based on the Unity 3D engine. In Computational and Information Sciences (ICCIS), 2011 International Conference on (pp. 875-877). IEEE.
[28] Mattingly, W. A., Chang, D. J., Paris, R., Smith, N., Blevins, J., & Ouyang, M. (2012, July). Robot design using Unity for computer games and robotic simulations. In Computer Games (CGAMES), 2012 17th International Conference on (pp. 56-59). IEEE.
[29] Pires, F. A., Santos, W. M., Andrade, K. D. O., Caurin, G. A., & Siqueira, A. A. (2014, May). Robotic platform for telerehabilitation studies based on unity game engine. In Serious Games and Applications for Health (SeGAH), 2014 IEEE 3rd International Conference on (pp. 1-6). IEEE.
[30] Bartneck, C., Soucy, M., Fleuret, K., & Sandoval, E. B. (2015, August). The robot engine—Making the unity 3D game engine work for HRI. In Robot and Human Interactive Communication (RO-MAN), 2015 24th IEEE International Symposium on (pp. 431-437). IEEE.
[31] Zhong, H., & Xiao, J. (2015, September). Apply technology acceptance model with big data analytics and unity game engine. In Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on (pp. 19-24). IEEE.
[32] Adinandra, S., Adhilaga, N. A., & Erfawan, D. (2015, October). WayBot: A low cost manipulator for playing Javanese puppet. In Information Technology and Electrical Engineering (ICITEE), 2015 7th International Conference on(pp. 376-381). IEEE.
[33] Ahmed, M., Idrees, M., ul Abideen, Z., Mumtaz, R., & Khalique, S. (2016, July). Deaf talk using 3D animated sign language: A sign language interpreter using Microsoft′s kinect v2. In SAI Computing Conference (SAI), 2016 (pp. 330-335). IEEE.
[34] Liu, L., & Mehrotra, S. (2016, August). Patient walk detection in hospital room using Microsoft Kinect V2. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 4395-4398). IEEE.
[35] Samoil, S., & Yanushkevich, S. N. (2016, July). Multispectral hand recognition using the Kinect v2 sensor. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 4258-4264). IEEE.
[36] Noonan, P. J., Ma, J., Cole, D., Howard, J., Hallett, W. A., Glocker, B., & Gunn, R. (2015, October). Simultaneous multiple kinect v2 for extended field of view motion tracking. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2015 IEEE (pp. 1-4). IEEE.
[37] Darby, J., Sánchez, M. B., Butler, P. B., & Loram, I. D. (2016). An evaluation of 3D head pose estimation using the Microsoft Kinect v2. Gait & posture, 48, 83-88.
[38] Capecci, M., Ceravolo, M. G., Ferracuti, F., Iarlori, S., Longhi, S., Romeo, L., ... & Verdini, F. (2016, August). Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5409-5412). IEEE.
[39] Corti, A., Giancola, S., Mainetti, G., & Sala, R. (2016). A metrological characterization of the Kinect V2 time-of-flight camera. Robotics and Autonomous Systems, 75, 584-594.
[40] Ning, X., & Guo, G. (2013). Assessing spinal loading using the Kinect depth sensor: A feasibility study. IEEE Sensors Journal, 13(4), 1139-1140.
[41] Wang, J., Xiong, Z., Wang, Z., Zhang, Y., & Wu, F. (2016). FPGA Design and Implementation of Kinect-Like Depth Sensing. IEEE Transactions on Circuits and Systems for Video Technology, 26(6), 1175-1186.
[42] Stommel, M., Beetz, M., & Xu, W. (2014). Inpainting of Missing Values in the Kinect Sensor′s Depth Maps Based on Background Estimates. IEEE Sensors Journal, 14(4), 1107-1116.
[43] Liu, K., Chen, C., Jafari, R., & Kehtarnavaz, N. (2014). Fusion of inertial and depth sensor data for robust hand gesture recognition. IEEE Sensors Journal, 14(6), 1898-1903.
[44] Landau, M. J., Choo, B. Y., & Beling, P. A. (2016). Simulating kinect infrared and depth images. IEEE transactions on cybernetics, 46(12), 3018-3031.
[45] Hou, J., Gao, H., & Li, X. (2016). Dsets-dbscan: a parameter-free clustering algorithm. IEEE Transactions on Image Processing, 25(7), 3182-3193.
[46] Edla, D. R., Jana, P. K., & Member, I. S. (2012). A prototype-based modified DBSCAN for gene clustering. Procedia Technology, 6, 485-492.
[47] Kumar, K. M., & Reddy, A. R. M. (2016). A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recognition, 58, 39-48.
[48] Dudik, J. M., Kurosu, A., Coyle, J. L., & Sejdić, E. (2015). A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals. Computers in biology and medicine, 59, 10-18.
[49] Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., & Shao, L. (2016). Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm. IEEE Transactions on Image Processing, 25(12), 5933-5942.
[50] D’Orazio, T., Marani, R., Renó, V., & Cicirelli, G. (2016). Recent trends in gesture recognition: how depth data has improved classical approaches. Image and Vision Computing, 52, 56-72.
[51] Ibañez, R., Soria, Á., Teyseyre, A., Rodríguez, G., & Campo, M. (2017). Approximate string matching: A lightweight approach to recognize gestures with Kinect. Pattern Recognition, 62, 73-86.
[52] Cheng, H., Yang, L., & Liu, Z. (2016). Survey on 3D hand gesture recognition. IEEE Transactions on Circuits and Systems for Video Technology, 26(9), 1659-1673.
[53] Ren, Z., Yuan, J., Meng, J., & Zhang, Z. (2013). Robust part-based hand gesture recognition using kinect sensor. IEEE transactions on multimedia, 15(5), 1110-1120.
[54] Zhou, Y., Jiang, G., & Lin, Y. (2016). A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recognition, 49, 102-114.
[55] Cheng, H., Dai, Z., Liu, Z., & Zhao, Y. (2016). An image-to-class dynamic time warping approach for both 3D static and trajectory hand gesture recognition. Pattern Recognition, 55, 137-147.
[56] Plouffe, G., & Cretu, A. M. (2016). Static and dynamic hand gesture recognition in depth data using dynamic time warping. IEEE transactions on instrumentation and measurement, 65(2), 305-316.
[57] Wang, C., Liu, Z., & Chan, S. C. (2015). Superpixel-based hand gesture recognition with kinect depth camera. IEEE transactions on multimedia, 17(1), 29-39.
[58] Theodoridis, S., Pikrakis, A., Koutroumbas, K., & Cavouras, D. (2010). Introduction to pattern recognition: a matlab approach. Academic Press.
[59] Nguyen-Dinh, L. V., Roggen, D., Calatroni, A., & Tröster, G. (2012, November). Improving online gesture recognition with template matching methods in accelerometer data. In Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on (pp. 831-836). IEEE.
[60] Yun, L., Lifeng, Z., & Shujun, Z. (2012). A hand gesture recognition method based on multi-feature fusion and template matching. Procedia Engineering, 29, 1678-1684.
[61] Rose, E. J., Racadio, R., Wong, K., Nguyen, S., Kim, J., & Zahler, A. (2017). Community-Based User Experience: Evaluating the Usability of Health Insurance Information with Immigrant Patients. IEEE Transactions on Professional Communication, 60(2), 214-231.
[62] Zhou, F., Ji, Y., & Jiao, R. J. (2014). Prospect-theoretic modeling of customer affective-cognitive decisions under uncertainty for user experience design. IEEE Transactions on Human-Machine Systems, 44(4), 468-483.
[63] Bao, Y., Wu, H., & Liu, X. (2017). From Prediction to Action: Improving User Experience with Data-Driven Resource Allocation. IEEE Journal on Selected Areas in Communications.
[64] Rauschenberger, M., Schrepp, M., Cota, M. P., Olschner, S., & Thomaschewski, J. (2003). Efficient measurement of the user experience of interactive products. How to use the user experience questionnaire (ueq). Example: spanish language version. International Journal of Artificial Intelligence and Interactive Multimedia. 2003; 2 (1): 39-45.
[65] https://docs.unity3d.com/ScriptReference/ParticleSystem.html access on July 18th 2017
[66] http://homes.cs.washington.edu/~edzhang/tutorials/kinect2/kinect3.html access on July 18th 2017
[67] https://www.freesoundeffects.com/ access on July 18th 2017
[68] http://www.fromtexttospeech.com/ access on July 18th 2017
[69] https://goo.gl/forms/HFlGdjEwjpvQMhO32 access on July 18th 2017
[70] http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/ access on July 18th 2017

指導教授

施國琛、Fitri Utaminingrum(Timothy K.Shih Fitri Utaminingrum)

審核日期

2017-7-27

推文