臺灣博碩士論文加值系統

本研究旨在提出結合雲端運算及影像辨識之行動商品比價系統，因應現今物價與人民平均所得之比例逐漸失衡，商品比價成為民眾重視的一項需求，本系統提供使用者便捷的操作模式，使用者僅需利用行動裝置之照相功能，以任意角度拍下商品照片後上傳至雲端，系統即會透過雲端上部署之影像辨識比對技術取得符合之商品，並在合理時間內將比價資訊回傳給使用者。
辨識技術在本研究可分為三階段，在進行各階段辨識前，系統會先透過前處理模組去除商品影像背景雜訊並取得商品外觀輪廓，在第一階段辨識採用傅立葉描述子 (Fourier Descriptor，FD) 進行商品之輪廓外觀辨識以篩選出外觀符合之商品，第二階段為本研究所提出之以色調為基礎之圖像分割法 (Hue-Based Image Segmentation，HBIS)，將針對篩選出之商品內部顏色進行辨識比對，且為了更進一步提升辨識之準確度，本研究加入第三階段加速穩健特徵（Speeded Up Robust Features，SURF），以比對更細部之商品內部區域特徵，系統透過上述三階段之辨識模式，並加入雲端平行與分散式運算之特性，可讓使用者快速取得符合之商品資訊。
本研究透過實際商品樣本蒐集與測量，根據實驗結果，在辨識後商品排名第一名即為正確商品之條件下，採用FD+HBIS兩階段辨識模式之商品辨識準確率可達86.85%，加入第三階段SURF之影像區域比對特性，最佳辨識率可達100%，且透過雲端系統可在0.953秒內完成商品辨識，快速將最符合之商品資訊回傳給使用者，如此可滿足使用者以實惠之價格購得所需商品。

Due to the rising price of merchandise, parity between merchandise has become an important demand. This study provides a convenient operation of the mobile parity system for users to compare price of merchandise. Users only use mobile device function such as camera and upload photo to the parity system after taking merchandise photo at any angle. Users will get the parity information of merchandise immediately through image recognition technology and cloud computing.
The image recognition technology can be divided into three phases. The first phase uses “Fourier Descriptors” (FD) to filter out the contour of the merchandise. The second phase uses “Hue-Based Image Segmentation” (HBIS) approach for matching merchandise color. The third phase uses “Speeded Up Robust Features” (SURF) to match the characteristics of the merchandise region in detail.
The findings are the following: In the condition of the correct merchandise that will ranked first after three phases recognition, the recognition rate can be up to 90% with a 1-second by cloud computing. Finally, the findings will proved this study’s mobile parity system is feasible in the real world.

一、 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 論文架構 3
二、 相關研究 4
2-1 雲端運算 (Cloud Computing) 4
2-1-1 雲端運算之必要特徵 4
2-2 Hadoop雲端架構 5
2-2-1 Job Tracker 6
2-2-2 Task Tracker 7
2-3 影像辨識 (Image Recognition) 8
2-3-1 影像輸入 8
2-3-2 影像前處理 8
2-3-3 特徵擷取 9
2-3-4 影像分類 10
2-3-5 辨識比對 10
2-4 影像辨識技術結合行動應用程式 10
三、 行動比價系統 13
3-1 系統前提假設 13
3-2 系統角色 13
3-2-1 購物網站業者 13
3-2-2 行動裝置使用者 14
3-3 系統架構 15
3-3-1商品資料搜集子系統 16
3-3-1-1 各購物網站商品資訊擷取模組 16
3-3-1-2 影像前處理模組 17
3-3-1-3 影像輪廓特徵擷取模組 21
3-3-1-4 影像顏色特徵擷取模組 24
3-3-1-5 影像區域特徵擷取模組 28
3-3-2商品比價子系統 35
3-3-2-1 影像輸入模組 35
3-3-2-2 商品比價模組 35
3-3-3 雲端伺服器架構 38
四、 實驗方法與數值分析 40
4-1 實驗設計 40
4-1-1 商品樣本資料建立 40
4-1-2 商品影像比對 42
4-1-3 比對辨識模式 43
4-2 實驗結果分析 44
4-2-1 實驗次數收斂值分析 44
4-2-2 辨識模式一（FD）效能分析 46
4-2-3 辨識模式二（HBIS）效能分析 54
4-2-4 辨識模式三（FD+HBIS）效能分析 62
4-2-5 辨識模式四（FD+HBIS+SURF）效能分析 71
4-2-6 各模式辨識演算法比較分析 78
4-2-7 資料庫商品數量對辨識率之影響分析 81
五、 結論 85
參考文獻 88
附錄、 英文縮寫名稱對照表 92

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