臺灣博碩士論文加值系統

在傳統教室上課，當教師使用人工式點名，造成教師授課時間減短；但應用人臉影像辨識於課堂點名系統時，系統須要先收集每個學生人臉資訊，這可能導致耗費過多時於建置系統，因此本論文提出一個基於分群法技術應用人臉影像辨識於學生出席自動記錄系統，首先利用直方圖等化技術解決明亮度太暗或太亮問題，接著利用局部二值化樣本方法抽取邊緣特徵，在應用K個親和傳遞分群法於邊緣特徵建立K個親和傳遞分群模式，經本研究證實K個親和傳遞分群法優於其他本研究所探討之分群法，並可應用於學習者人臉分群。

The thesis proposes a face recognition technique based on clustering technology. It first adopts the histogram-equalization scheme to solve the high brightness problem. That is, a face image may be highly dark or bright. Subsequently, the proposed technique uses the local-binary-pattern (LBP) method to extract edge features for a face image. These edge features of an image can be formed as a feature vector for the image. It then utilizes the K-affinity propagation (K-AP) scheme to generate a K-AP clustering model for a set of feature vectors of images. Experimental results show that the performance of the proposed technique is better than that of other clustering methods under consideration here. Consequently, the technique can be applied to face-recognition applications.

中文摘要 i
ABSTRACT ii
致謝 iii
表目錄 vi
圖目錄 vii
第一章、緒論 1
1.1 動機 1
1.2 目的 3
1.3 相關研究 5
1.4 論文架構 7
第二章、文獻探討 8
2.1. 前置處理 8
2.1.1 彩色影像轉灰階影像 8
2.1.2 矩形特徴 9
2.1.3 AdaBoost演算法 11
2.1.4 影像的縮放 13
2.2. 直方圖 14
2.2.1 直方圖等化 15
2.2.2 對數正態分佈圖 16
2.2.3 常態分佈圖 17
2.2.4 直方圖截斷和拉伸 18
2.2.5 指數分佈圖 19
2.3. Retinex方法 20
2.3.1 單尺度Retinex演算法 21
2.3.2 多尺度Retinex演算法 24
2.3.3 適性化單尺度演算法 25
2.3.4 單尺度自商影像演算法 29
2.3.5 等向性擴散 31
2.3.6 非等向性擴散演算法 32
2.3.7 離散餘弦變換演算法 33
2.3.8 同態濾波演算法 36
2.3.9 小波轉換濾波演算法 38
2.3.10 小波去雜訊 39
2.3.11 區域性對比強化演算法 41
2.4. 邊緣偵測 42
2.4.1 局部二值化樣本 42
2.4.2 加伯濾波器 44
2.4.3 Sobel filter 46
2.5. 分群法 48
2.5.1 K-means分群演算法 48
2.5.2 模糊C-means分群演算法 50
2.5.3 K-medoid 分群演算法 52
2.5.4 K-Affnity Propagation演算法 53
2.5.5 自我組織圖分群演算法 56
2.5.6 最大期望演算法 59
2.6. 效能評估 61
第三章、研究方法 62
3.1. 本論文所提出之人臉分群流程 62
3.2. 人臉分群模組 64
3.3. 人臉分群法模組 69
第四章、實驗結果與討論 73
4.1. 人臉影像資料庫與實驗環境 73
4.1.1 Feedtum人臉資料庫 74
4.1.2 NFUIM-SC（Seminar Class Class）人臉資料庫 75
4.1.3 資料庫差異比較 76
4.2. 人臉分群辨識結果 77
4.2.1 Feedtum Faces databases實驗結果 78
4.2.2 NFUIM-SC（Seminar Class）實驗結果 79
第五章、結論 81
5.1 研究限制 82
5.2 未來研究 84
第六章、參考文獻 85
附錄一 94
附錄二 96

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