請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41922完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳良基(Liang-Gee Cheng) | |
| dc.contributor.author | Chia-Hua Lin | en |
| dc.contributor.author | 林家華 | zh_TW |
| dc.date.accessioned | 2021-06-15T00:37:35Z | - |
| dc.date.available | 2008-11-25 | |
| dc.date.copyright | 2008-11-25 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-11-19 | |
| dc.identifier.citation | [1] Tao Zhao and R. Nevatia, “Tracking multiple humans in complex situations,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1208–1221, Sep 2004. [2] Liang Wang, “From blob metrics to posture classification to activity profiling,” in 18th IEEE International Conference on Pattern Recognition, 2006, vol. 4, pp. 736–739. [3] Stephen J. McKenna, Sumer Jabri, Zoran Duric, Harry Wechsler, and Azriel Rosenfeld, “Tracking groups of people,” Computer Vision and Image Understanding: CVIU, vol. 80, no. 1, pp. 42–56, 2000. [4] P. Guha, A. Mukerjee, K.S. Venkatesh, and P. Mitra, “Activity discovery from surveillance videos,” in 18th IEEE International Conference on Pattern Recognition, 2006, vol. 1, pp. 433–436. [5] S.-G. Miaou, P.-H. Sung, and C.-Y. Huang, “A customized human fall detection system using omni-camera images and personal information,” in 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006, pp. 39–42. [6] Junwen Wu and M.M. Trivedi, “Visual modules for head gesture analysis in intelligent vehicle systems,” in 2006 IEEE Intelligent Vehicles Symposium, 2006, pp. 13–18. [7] Paul Robertson, Robert Laddaga, and Max Van Kleek, “Virtual mouse vision based interface,” in Proceedings of the 9th International Conference on Intelligent User Interfaces, 2004, pp. 177–183. [8] Ralf M. Philipp et al., “A 128£128 33mw 30frames/s single-chip stereo imager,” in ISSCC Dig. Tech. Papers, 2006, pp. 506–507. [9] S. Kyo, T. Koga, S. Okazaki, R. Uchida, S. Yoshimoto, and I. Kuroda, “A 51.2 GOPS scalable video recognition processor for intelligent cruise control based on a linear array of 128 4-way VLIW processing elements,” in ISSCC Dig. Tech. Papers, 2003, vol. 1, pp. 48–49. [10] A. Abbo, R. Kleihorst, V. Choudhary, L. Sevat, P. Wielage, S. Mouy, and M. Heijligers, “XETAL-II: A 107 GOPS, 600mW massively-parallel processor for video scene analysis,” in ISSCC Dig. Tech. Papers, 2007, vol. 1, pp. 270–271. [11] P. Dudek and P.J. Hicks, “A general-purpose processor-per-pixel analog SIMD vision chip,” IEEE Trans. Circuits and Systems: I, vol. 52, no. 1, pp. 13–20, Jan. 2005. [12] Shorin Kyo and S. Okazaki, “In-vehicle vision processors for driver assistance systems,” in Proceedings of Asia and South Pacific Design Automation Conference, 2008, pp. 383–388. [13] P.P. Jonker, “Why linear arrays are better image processors,” in Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994, vol. 3, pp. 334–338. [14] B. Khailany, T. Williams, J. Lin, E. Long, M. Rygh, D. Tovey, and W.J Daly, “A programmable 512 GOPS stream processor for signal, image, and video processing,” in Proceedings of IEEE International Solid-State Circuits Conference, 2007, pp. 272–602. [15] Open source computer vision library, http://sourceforge.net/projects/opencvlibrary/. [16] http://sourceforge.net/project/stats/detail.php?group id=22870 &ugn=opencvlibrary&type=prdownload&mode=alltime&package id=0. [17] http://www.cs.iit.edu/ agam/cs512/lect-notes/opencv-intro/index.html. [18] http://www.cs.stanford.edu/group/roadrunner/stanley.html. [19] http://cell.fixstars.com/opencv/index.php/OpenCV on the Cell. [20] http://www5.epfl.ch/swis/page3080.html. [21] Paul Viola and Michael Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, vol. 1, pp. I511–I518. [22] http://focus.ti.com/lit/an/spra651/spra651.pdf. [23] http://focus.ti.com/lit/an/spraak8a/spraak8a.pdf. [24] T. Kuno, H. Sugiura, and N. Matoba, “A new automatic exposure system for digital still cameras,” in Proceedings of IEEE Transactions on Consumer Electronics., 1998, vol. 44, pp. 192–199. [25] http://www.imageval.com/public/Products/ISET/ISET Introduction/AutoExposure.htm. [26] Jun yan Huo, Yi lin Chang, Jing Wang, and Xiao xia Wei, “Robust automatic white balance algorithm using gray color points in images,” in Proceedings of IEEE Transactions on Consumer Electronics., 2006, vol. 52, pp. 541–546. [27] http://scien.stanford.edu/class/psych221/projects/00/trek/GWimages.html. [28] Jen-ChuanWang, Der-Song Su, Den-Jen Hwung, and Ji-Chien Lee, “A single-chip ccd signal processor for digital still cameras,” in Proceedings of IEEE Transactions on Consumer Electronics., 1994, vol. 40, pp. 476–483. [29] James E. Adams, “Interactions between color plane interpolation and other image processing functions in electronic photography,” in Proceedings of SPIE, 1995, vol. 2416, pp. 144–151. [30] Claude A. Laroche and Mark A. Prescott, “Apparatus and method for adaptively interpolating a full color image utilizing luminance gradients,” in US Patent 5373322. [31] Je-Ho Lee, Kun-Sop Kim, Byung-Deok Nam, Jae-Chon Lee, Yong-Moo Kwon, and Hyoung-Gon Kim, “Implementation of a passive automatic focusing algorithm for digital still camera,” in Proceedings of IEEE Transactions on Consumer Electronics., 1995, vol. 41, pp. 449–454. [32] A. Sixsmith and N. Johnson, “A smart sensor to detect the falls of the elderly,” in Proceedings of IEEE on Pervasive Computing, 2004, pp. 42–47. [33] C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, “Pfinder: real-time tracking of the human body,” in Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, vol. 19, pp. 780–785. [34] Programmable DSP Platform for Digital Still Cameras TI, http://focus.ti.com/general/docs/techdocsabstract.tsp?abstractName=spra651. [35] R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, 1992. [36] D. Wang, “Unsupervised video segmentation based on watersheds and temporal tracking,” in Proceedings of IEEE Transactions on Circuits and Systems for Video Technology., 1998, vol. 8, pp. 539–546. [37] Shao-Yi Chien, Video Segmentation: Algorithms, Hardware Architecture, and Applications, 2003. [38] Shorin Kyo, Shin’ichiro Okazaki, and Tamio Arai, “An integrated memory array processor for embedded image recognition systems,” in Proceedings of IEEE Transactions on Computers, 2007, vol. 56, pp. 622–634. [39] S. Przybylski, M. Horowitz, and J. Hennessy, “Performance tradeoffs in cache design,” in Proceedings of the 15th Annual International Symposium on Computer Architecture, 1988, pp. 290–298. [40] P.P. Jonker, “Why linear arrays are better image processors,” in Proceedings of the 12th IAPR International Comference on Pattern Recognition, 1994, vol. 3, pp. 334–338. [41] M. Hiromoto, K. Nakahara, H. Sugano, Y. Nakamura, and R. Miyamoto, “A specialized processor suitable for adaboost-based detection with haar-like features,” in Proceedings of IEEE conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8. [42] X. Bing Y. Wei and C. Chareonsak, “Fpga implementation of adaboost algorithm for detection of face biometrics,” in Proceedings of IEEE International Workshop on Biomedical Circuits and Systems, 2004, pp. 17–20. [43] Ming Yang, YingWu, J. Crenshaw, B. Augustine, and R. Mareachen, “Face detection for automatic exposure control in handheld camera,” in Proceedings of IEEE International Conference on Computer Vision System, 2006, pp. 17–17. [44] http://vasc.ri.cmu.edu/idb/html/face/. [45] H. Sugano and R. Miyamoto, “Parallel implementation of morphological processing on cell/be with opencv interface,” in Proceedings of 3rd International Symposium on Communications, Control and Signal Processing, 2008. ISCCSP 2008., 2008, pp. 578–583. [46] T. Theocharides, N. Vijaykrishnan, and M.J. Irwin, “A parallel architecture for hardware face detection,” in Proceedings of IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures, 2006, vol. 0, pp. 2–3. [47] Hung-Chih Lai, M. Savvides, and Tsuhan Chen, “Proposed fpga hardware architecture for high frame rate (>>100fps) face detection using feature cascade classifiers,” in Proceedings of First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007., 2007, vol. 0, pp. 1–6. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41922 | - |
| dc.description.abstract | 本篇論文中提出一個高效率的視覺信號處理器架構設計並支援OpenCV 函式庫，
此晶片運算輸出為64GOPS，功率消耗86.5mW，在UMC 90nm Logic&Mixed Mode 1P9M Low-K 製程下，面積為2.75mm x 2.75mm。 近年來，視訊分析技術在視訊應用上面扮演著越來越重要的角色，例如監視系 統、健康醫療照護、智慧型車輛駕駛系統等等，我們相信智慧型視訊分析技術會 是未來的發展趨勢。 Intel OpenCV 函式庫近期在相關研究領域上相當受歡迎，並創造出許多成功的 應用。本篇論文所提出的硬體架構可以提供一對一的函式轉換，讓原本在電腦端 執行的函式直接轉移到嵌入式系統中，這樣的硬體不但可以節省演算法的開發時 間，更可以加速產品上市的時間。 型態學上的運算是電腦視覺中最常被使用的函式，通常是許多影像辨識的前處理 過程。實驗結果顯示我們所提出的架構可以提供高運算輸出及低功率消耗，對於 一張1024 x 768 的灰階影像，運算速度可達每秒兩百張影像，同時，相較於目前 可見的其他影像處理器，此架構僅需相當小的面積。 除了型態學的運算之外，快速的物件偵測是電腦視覺領域上的另一個挑戰。Viola 及Jones 提出了一個快速偵測物件的演算法，被OpenCV 採用，我們也將此演 算法針對我們的硬體作最佳化的設計，我們採用CMU+MIT 的臉部偵測的標準 測試圖樣來測試我們修改過後的演算法， 119 張影像中含有513 張臉，實驗結 果顯示我們的偵測率為87.3%，運算速度在320x240 的影像大小下可達每秒10 張。 | zh_TW |
| dc.description.abstract | An efficient architecture design of vision signal processor with OpenCV library support
is presented in this thesis. It is a 64GOPS, 86.5mW vision processor which is implemented on a 2.75mm£2.75mm die in a UMC 90nm Logic&Mixed-Mode 1P9M Low-K Process. In the resent decades, video analysis technology plays more and more important role in many vision applications, such as surveillance system, healthcare, intelligent vehicle system and so on. It is believed that intelligent video analysis technology will must be the trends of development. Nowadays, Intel OpenCV library is popular in the research domain and creates lot’s of successful applications. Our hardware can provide one to one function mapping from PC OpenCV library to embedded system. With this hardware, the implementation time can be saved and speed up the product become available in the market. One of the most frequently used operations in image recognitions morphological processing, which is often adopted for pre-processing of various applications based on image recognition. Experimental result shows our proposed architecture performs high computation throughput and low power consumption. It can process 1024£768 8-bit gray level image with more than 200 frames per second and the area is quite small compared to state-of-the-arts. Robust and rapid object detection is the other challenge in the field of computer vision. A object detection algorithm proposed by Viola and Joses is implemented in our design. We optimize the algorithm to our hardware with low performance drop. Detection rate for CMU+MIT test database which consists of 119 images with 513 labeled frontal faces is 87.3%. The processing speed is 10 frames per second with 320£240 8-bit gray level image | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T00:37:35Z (GMT). No. of bitstreams: 1 ntu-97-R95943021-1.pdf: 4940213 bytes, checksum: 7b35393b017d2026ca47ba7f78eb788e (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Current Development of Vision Processor . . . . . . . . . . . . . . . . 2 1.3 Design Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Overview of OpenCV Library 7 2.1 Introduction to OpenCV . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 OpenCV Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Application (I) —Object detection . . . . . . . . . . . . . . . . . . . 10 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3.2 Adaboost Algorithm . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.3 Integral Image . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3.4 Cascade Classifier . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 Application (II)—Image Signal Processing . . . . . . . . . . . . . . . 18 2.5 Application (III)—Fall Detection . . . . . . . . . . . . . . . . . . . . 23 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 Design of Vision Signal Processor 27 3.1 Design Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Design Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4 Benchmark Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Morphological Operation . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Haar-feature Object Detection . . . . . . . . . . . . . . . . . . 31 3.4.3 Architecture Design Strategy . . . . . . . . . . . . . . . . . . . 32 3.4.4 Analysis of Tile-based Processing . . . . . . . . . . . . . . . . 36 3.5 Design Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.5.1 Tile-based image stream processing . . . . . . . . . . . . . . . 38 3.5.2 Integral intelligent image processing unit . . . . . . . . . . . . 39 3.5.3 Parallel mask conditional operation . . . . . . . . . . . . . . . 40 3.6 Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.6.1 Programming Model . . . . . . . . . . . . . . . . . . . . . . . 43 3.6.2 Tile Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.6.3 SIMD Image Processor . . . . . . . . . . . . . . . . . . . . . . 47 3.6.4 Instruction Set Architecture . . . . . . . . . . . . . . . . . . . 52 3.7 Optimization of Haar-Feature Object Detection Algorithm for Our Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4 Chip Implementation 63 4.1 Design Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 FPGA Verification and Demonstration . . . . . . . . . . . . . . . . . . 65 4.2.1 FPGA Verification . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2.2 FPGA Demonstration . . . . . . . . . . . . . . . . . . . . . . 66 4.3 Chip Specification and Layout . . . . . . . . . . . . . . . . . . . . . . 67 4.4 Experimental Result and Comparison . . . . . . . . . . . . . . . . . . 67 5 Conclusion 79 Reference 81 | |
| dc.language.iso | en | |
| dc.subject | 視訊信號 | zh_TW |
| dc.subject | 臉部偵測 | zh_TW |
| dc.subject | 處理器 | zh_TW |
| dc.subject | face detection | en |
| dc.subject | opencv | en |
| dc.subject | processor | en |
| dc.subject | vision | en |
| dc.title | 支援OpenCV函式庫視訊信號處理器之架構設計與實作 | zh_TW |
| dc.title | Design and Implementation of Vision Signal Processor with OpenCV Library Support | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊佳玲(Chia-Lin Yang),賴永康(Yeong-Kang Lai),簡韶逸(Shao-Yi Chien) | |
| dc.subject.keyword | 視訊信號,處理器,臉部偵測, | zh_TW |
| dc.subject.keyword | vision,processor,opencv,face detection, | en |
| dc.relation.page | 85 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-11-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| 顯示於系所單位： | 電子工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-97-1.pdf 未授權公開取用 | 4.82 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。