由於Gabor小波(Wavelet)具有多頻濾波能力之優異特性，以Gabor小波為影像表示(Representation)之辨識方法，被廣泛應用於臉型辨識。雖然，過去有諸多以Gabor小波函數，調整濾波器的中心頻率(Center frequency)與方向(Filter orientation)為基礎，進行影像辨識之研究，但尚有許多主題值得深入探討。
以Gabor小波為基礎之影像辨識，傳統方法大部分注重特徵擷取或降階處理。但對於完整影像辨識方法，應包含影像前處理、影像表示、特徵擷取(Feature extraction)、降階處理(Dimension reduction)與分類模式(Classification model)。本文將針對紅外線影像(Infrared image)與光學影像(Optical image)兩種影像類別，以Gabor小波為基礎，建置一套影像識別系統。因此，提出一種強化Gabor小波之表示方法，以全部原始影像之頻率響應為基準，去設計選擇濾波器之中心頻率與濾波器之方向變化之數量，做為Gabor小波所需影像表示之依據。其次，在分類模式方面，發展兩種方法去增強辨識效果。修正K值最接近鄰近規則(Modified K-nearest neighbor rule)，是以原始影像間彼此關聯性(Cross correlation)大小，來決定對距離上之K值選擇。另外一個辨識模式，是以一個自動調整方式(Self-adaptive)用於以輻射函數為基底之類神經網路(Radial basis function networks) ，此方法可經由訓練過程，選擇隱藏層(Hidden neurons)之數量增減，藉由逼近誤差(Approximation error)去防範過度訓練(Over fitting)或訓練不足(Under fitting)。
最後，在紅外線影像與光學影像實用上，所提出之辨識系統，分成訓練影像與測試影像，經由試驗結果，顯示具有良好辨識效果，證明此方法可行性。

Gabor wavelets have been successfully applied in various areas from image processing to pattern recognition. This success is mostly due to the fact that Gabor wavelets are equipped with a multi-channel filter capability such that a desired representation of the filter banks can be developed through selecting the parameters, i.e., the center frequency and the orientation of the filter. Although many types of image recognition based on Gabor wavelets have been studied and reported, there are still many valuable topics to be further investigated.
The conventional image recognition based on Gabor wavelets is focusing on feature extraction in order to reduce the dimension of the training images. However, this feature extraction is only one of the processes in pattern recognition. For the recognition of the infrared image and optical image, it includes image representation, feature extraction or dimension reduction, classification technique, and the object character. The aim of this dissertation is to construct an image recognition system based on Gabor wavelets for infrared image and optical image. Therefore, An enhanced representation of the Gabor wavelets is proposed, in which the properties of Gaussian mask in Gabor wavelets is developed to enhance the enveloped function, and simultaneously the parameters of the filter based on Gabor wavelets is designed depending on the frequency response of the training images.
In addition, the classification technique is important in the pattern recognition. In this dissertation, two classification methods are developed. The modified K-nearest neighbor rule is employed, in which the proposed modified rule combined with variance of the training images is used to find the optimal K value of the nearest distance between training images and testing images. For the neural network classification model, the self-adaptive radial basis function networks is employed, in which the self-adaptive networks has the property that during the training iteration the number of hidden neurons can be either increased or decreased according to the approximation error to prevent over fitting or under fitting.
Furthermore, the practical application is also an important issue on the infrared image and optical image. Almost all of the reported Gabor-based image recognitions are applied on the face image. In order to consider the different kind of image, this dissertation proposes an image recognition method based on the Gabor wavelets, which can be applied on both the infrared image and optical image. Some experiments including infrared image and optical image recognitions are given. The good performances are verified through using the proposed scheme in this dissertation.

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