比較於小波轉換在壓縮上的應用，這篇論文將小波轉換的研究重點，放置於紋理影像的辨識。採用的小波轉換基底有四個Haar、Daubechies’ four、5/3以及9//7小波轉換。Haar小波轉換，只取兩個運算元作為輸入，在概念上，是小波轉換中最易於瞭解的基底。而Daubechies’ four小波轉換，在小波轉換一開始廣為使用的基底。5/3、9/7小波轉換則是JPEG2000依據圖片壓縮率的需求而制訂的。 因此在眾多的小波基底中，我們採用這四個小波基底。一張影像以四個小波基底計算得到的特徵，利用圖訊識別的1-nn分類法，以及五種相似度量測方法，計算各個特徵之間的差異性，最後對這四個小波基底在分類上做效能評估。除了小波轉換，碎形維度也涵蓋在這篇論文中。經由碎形的概念，將一張平面影像視為2D到3D的物體，並計算維度，最後以圖訊識別量測碎形特徵的效能。

Many proficient techniques classify texture images with high performance. However, under the unknown result, we attempt to apply a new method, wavelets, adopted by JPEG2000 and MPGE4 to classify texture images in the thesis. Fractal dimensions describe texture images by a floating point between 2 and 3. The Fourier power spectrum and the box counting method are adopted to compute two fractal dimensions. Experiments will not only include wavelets, but measure the fractal dimensions of Brodatz database.

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