影像色彩與紋理特徵擷取硬體加速器

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姓名

何崇睿(Chung-Jui Ho) 查詢紙本館藏

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資訊工程學系

論文名稱

影像色彩與紋理特徵擷取硬體加速器
(Embedded Hardware Design of Color/Texture Feature Extractor)

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摘要(中)

特徵擷取是影像分析和辨識中非常重要的一環，越來越多的應用需求朝向即時取得特徵，自組織對映神經網路以及局部二值型態雖然都具有很強的特徵描述能力，但是在軟體執行上的速度無法達到即時需求。因此本研究設計了一個影像色彩和紋理特徵擷取硬體加速器，使用硬體的平行化以及管線化優勢，以加速影像特徵擷取速度。此一硬體加速器具有彈性架構，可提供全域和區域特徵擷取兩個模式，針對SOM色彩特徵量化，我們也設計了2×2以及4×4輸出神經元，提供不同色彩量化硬體架構。實驗驗證對640×480的影像進行特徵擷取，我們的硬體加速器可以在100MHz的系統時脈條件下，相對於PC，可達到10.5倍的色彩特徵擷取速度提升；紋理特徵擷取也比PC快1.875倍的速度，展現了特徵擷取硬體加速器低功耗、體積小且高效率的優勢。

摘要(英)

Feature extraction is crucial to image analysis and identification, and applications that need instantaneous feature extraction are increasing. Self-organizing map (SOM) neural networks and local binary patterns possess outstanding feature description capability. However, the computation speed of existent software cannot achieve instantaneous extraction using these two methods. Therefore, this study designed an image color and texture feature extraction hardware accelerator, using the parallelizability and pipelining of hardware to increase the speed of image feature extraction. The proposed accelerator features a flexible framework to enable global and regional feature extraction. To quantize the color features of an SOM, we also designed 2×2 and 4×4 output neurons for different color quantization hardware frameworks. Experiments were performed by extracting features from 640×480 images. Under a system clock rate of 100 MHz, the color feature extraction speed of the proposed accelerator was 10.5 times that of a conventional personal computer. The texture feature extraction speed of the accelerator was also 1.875 times that of the personal computer. The results demonstrated that the proposed hardware accelerator is advantageous for its low energy consumption, small volume, and high efficiency.

關鍵字(中)

★ 特徵擷取
★ 硬體加速
★ 自組織對映神經網路
★ 局部二值型態
★ 全域特徵
★ 區域特徵

關鍵字(英)

論文目次

摘要 V
Abstract VI
謝誌 VII
目錄 IX
圖目錄 XI
表目錄 XIII
第一章、緒論 1
1.1 研究背景 1
1.2 研究目標 3
1.3 論文架構 4
第二章、技術回顧 5
2.1 LBP特徵 5
2.1.1 LBP特徵演算法 5
2.1.2 LBP特徵演算法硬體 8
2.2 SOM神經網路 9
2.2.1 SOM神經網路演算法 9
2.2.2 SOM神經網路硬體 12
2.3 嵌入式硬體設計方法論 13
2.3.1 階層式模組化設計 14
2.3.2 離散事件建模 16
2.3.3 硬體高階合成 19
第三章、影像色彩與紋理特徵擷取硬體加速器設計 21
3.1 系統架構 21
3.2 特徵擷取離散事件建模 24
3.2.1 區域色彩特徵擷取建模 25
3.2.2 全域色彩特徵擷取建模 26
3.2.3 區域紋理特徵擷取建模 27
3.2.4 全域紋理特徵擷取建模 29
3.3 特徵擷取方法設計說明 29
3.3.1 SOM色彩特徵擷取方法 29
3.3.2 LBP紋理特徵擷取方法 30
3.3.3 全域和區域特徵擷取方法 31
3.4 特徵擷取管線控制器設計 32
3.4.1 色彩特徵擷取管線控制 33
3.4.2 紋理特徵擷取管線控制 33
第四章、實驗結果 35
4.1 實驗軟硬體介紹 35
4.2 硬體合成與驗證 35
4.2.1 色彩特徵擷取合成模組 36
4.2.2 紋理特徵擷取合成模組 38
4.2.3 硬體合成資源 40
4.2.4 軟硬體結果評估 41
4.3 硬體加速驗證 43
第五章、結論與未來展望 44
5.1 結論 44
5.2 未來展望 45
參考文獻 46

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指導教授

陳慶瀚

審核日期

2019-7-18

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