April 2014
Beginner to intermediate
830 pages
15h 21m
Chinese
Content preview from Mathematica 錦囊妙計
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解決方案
以下介紹的方法中，使用了
主成分分析
（
Principal components analysis
，PCA）及資訊
理論概念，將高維度的試驗影像映射至低維度空間，並保留主要資訊成分，如此即可找
出與輸入影像最接近的試驗影像。365
計算特徵影像及試驗影像的平均影像
將影像資料向量化，並將灰階值調整為
[0,1] 區間
計算特徵向量、平均影像向量及縮小為
M × M 系統的特徵向量，其中 M 為試驗影像個數
ch08.indd 362 2014/4/2 下午 12:24:16
8.8 以特徵向量（特徵影像）實作影像辨識
|
363
計算每個輸入影像的權重向量與可接受的門檻值，用來從eigenImageElements 中挑選匹配的影像
從試驗影像中判斷影像匹配程度的排序，從匹配程度最高至最低
[Page-366]
ch08.indd 363 2014/4/2 下午 12:24:17
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第八章
以下函數在輸入多影像的情況下仍然很有效率，因為只需以試驗影像計算eigenImageElements ...
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