請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53946完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善(Chiou-Shann Fuh) | |
| dc.contributor.author | Wei-Cheng Su | en |
| dc.contributor.author | 蘇唯誠 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:34:16Z | - |
| dc.date.available | 2015-07-29 | |
| dc.date.copyright | 2015-07-29 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-07-28 | |
| dc.identifier.citation | [1] Basler, “raL8192-80km,” http://www.baslerweb.com/en/products/line-scan-cameras/racer/ral8192-80km, 2015.
[2] A. Burnes, “Kepler for Every Gamer: Meet the New GeForce GTX 660 & 650,” http://www.geforce.com/whats-new/articles/geforce-gtx-660-650-launch, 2012. [3] K. M. He, J. Sun, and X. O. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 12, pp. 2341-2353, 2011. [4] K. M. He, J. Sun, and X. O. Tang, “Guided Image Filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 6, pp. 1397-1409, 2013. [5] NVIDIA, “NVIDIA’s Next Generation CUDATM Compute Architecture: FermiTM,” http://www.nvidia.com/content/pdf/fermi_white_papers/nvidia_fermi_compute_architecture_whitepaper.pdf, 2009. [6] NVIDIA, “NVIDIA’s Next Generation CUDATM Compute Architecture: KeplerTM GK110,” http://www.nvidia.com/content/PDF/kepler/NVIDIA-kepler-GK110-Architecture-Whitepaper.pdf, 2012. [7] N. Whitehead and A. Fit-Florea, “Precision & Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs,” https://developer.nvidia.com/sites/default/files/akamai/cuda/files/NVIDIA-CUDA-Floating-Point.pdf, 2015. [8] Wikipedia, “CUDA,” http://en.wikipedia.org/wiki/CUDA, 2015. [9] Wikipedia, “GeForce 600 Series,” http://en.wikipedia.org/wiki/GeForce_600_series, 2015. [10] Wikipedia, “Vignetting,” http://en.wikipedia.org/wiki/Vignetting, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53946 | - |
| dc.description.abstract | 本論文中探討了如何有效率的使用統一計算架構對線掃描影像進行加速處理。統一計算架構是一種在圖形處理器上實現平行化的工具，平行化過的程式可以有更好的執行速度。本論文中，我們比較了各種平行圖形處理的實現並將其技巧應用在工業缺陷檢測上。 | zh_TW |
| dc.description.abstract | We discuss how to accelerate the line-scan image processing by Compute Unified Device Architecture (CUDA) efficiently in this thesis. CUDA is a parallel implementation on Graphics Processing Unit (GPU). The parallel program could be faster than Central Processing Unit (CPU). In this thesis, we compare the performance of some parallel image processing implementations and apply the technology on Automatic Optical Inspection (AOI). | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:34:16Z (GMT). No. of bitstreams: 1 ntu-104-R02922114-1.pdf: 1980364 bytes, checksum: 886e405f5e1246be4e02fd6369633de9 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Related Works 3 2.1 Guided Image Filter 3 2.2 Fused Multiply-Add (FMA) 4 Chapter 3 Background 6 3.1 Line-Scan Camera 6 3.2 GTX 660 6 Chapter 4 Implementation and Result 8 4.1 Shading 8 4.1.1 CUDA Method 1 for Shading 11 4.1.2 CUDA Method 2 for Shading 14 4.2 Mean Filter 16 4.2.1 CUDA Method 1 for Mean Filter 18 4.2.2 CUDA Method 2 for Mean Filter 20 4.3 Sharpen 22 4.3.1 CUDA Method 1 for Sharpen 24 4.3.2 CUDA Method 2 for Sharpen 25 4.4 Contrast Stretching 27 4.4.1 CUDA Method 1 for Contrast Stretching 29 4.4.2 CUDA Method 2 for Contrast Stretching 31 4.5 Edge Enhancement 33 4.5.1 CUDA Method 1 for Edge Enhancement 34 4.5.2 CUDA Method 2 for Edge Enhancement 36 4.6 Guided Image Filter 38 4.6.1 CUDA Implementation for Guided Image Filter 39 4.7 Production Result 41 Chapter 5 Conclusion and Future Work 44 REFERENCES 45 | |
| dc.language.iso | en | |
| dc.subject | 統一計算架構(CUDA) | zh_TW |
| dc.subject | 線掃瞄影像 | zh_TW |
| dc.subject | Compute Unified Device Architecture (CUDA) | en |
| dc.subject | line-scan image | en |
| dc.title | 對線掃描影像處理使用CUDA加速 | zh_TW |
| dc.title | CUDA Acceleration on Image Processing for Line-Scan Image | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖本博,陳世亮 | |
| dc.subject.keyword | 線掃瞄影像,統一計算架構(CUDA), | zh_TW |
| dc.subject.keyword | line-scan image,Compute Unified Device Architecture (CUDA), | en |
| dc.relation.page | 46 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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