摘要
在研究了目标图像多尺度小波分解特性的基础上,提出了基于小波多尺度分解子带主成分的特征提取的算法。该算法利用图像在不同尺度的小波变换域中能量局部集中性,选择各子带能量较集中的局部小波系数构成图像目标特征向量。这种特征包含图像目标的主要边缘、纹理、灰度、结构等多种信息。由于对图像目标的特征信息的分布没有任何限制,因而适用于多种类型的图像的特征提取,可以解决单一特征提取方法中必须面对的所提取特征不明显的难点。这种特征向量对噪声有较好的鲁棒性。
Based on an analysis of the wavelet malfi-scale transform in target image, this paper puts forward a new method to extract the main features of the transform. It, in terms of the energy concentricity of the image＇s wavelet coefficients, selected the parts with concentrated energy to construct the feature vectors, which include most of the edge, texture, luminance and structure features. As there is no limit to the distribution of the image feature information, the method can be used in many kinds of image feature extraction, thus solving the problem of feature illegibility with which the single-feature extraction is confronted. In the experiment, normal white noises with different ranges were added to the images and the result approves that the feature vectors are robust to noise.
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2006年第1期85-89,共5页
Journal of National University of Defense Technology
关键词
小波多尺度分解
图像处理
特征提取
子带主成分
wavelet multi-scale transform
image processing
feature extraction
MCSF（main coefficient of sub-frequency）
