The main contributions of this thesis are as follows:(1) An edge feature detection model based on gradient and phase (GP model) is proposed.
本文的主要贡献如下：（1）提出了梯度和相位信息相结合的边缘特征检测模型—GP模型。
The fourth chapter discusses in detail a novel method for edge feature detection of images based on wavelet decomposition and reconstruction by means of interval biorthogonal wavelet.
第四章详细地讨论了利用区间双正交小波，根据小波分解和重构来提取图像的边缘特征的一种新方法。
This neural network pattern recognition can be applied to feature extraction, clustering analysis, edge detection, signal enhancement and noise suppression, data compression, such as various links.
这样神经网络可应用于模式识别的特征提取、聚类分析、边缘检测、信号增强以及噪声抑制、数据压缩等各个环节。
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