The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the origin...The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.展开更多
Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recogn...Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing. The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.展开更多
为了简单有效地提取图像重要特征信息,从而更好地提高检索图像的精度,提出了一种基于脉冲耦合神经网络(Pulse coupled neural networks,PCNN)的图像归一化转动惯量(Normalized moment of inertia,NMI)特征提取及检索算法.首先利用改进简...为了简单有效地提取图像重要特征信息,从而更好地提高检索图像的精度,提出了一种基于脉冲耦合神经网络(Pulse coupled neural networks,PCNN)的图像归一化转动惯量(Normalized moment of inertia,NMI)特征提取及检索算法.首先利用改进简化PCNN模型相似神经元同步时空特性及指数衰降机制将图像分解为具有相关性的二值系列图像,然后提取反映原始图像目标形状、结构分布二值系列图像的一维NMI特征矢量信号,并将其应用在图像检索中;同时,考虑到二值系列图像间的相关性及不同图像间NMI序列值的差异性,引入了马氏距离结合Pearson积矩相关法的综合相似性度量方法.实验结果表明,所提算法对图像特征矢量序列具有良好抗几何畸变不变特性及对图像表述的唯一性,且具有较好的图像检索效果.展开更多
基金the National Natural Science Foundation of China (60303029)
文摘The image shape feature can be described by the image Zernike moments. In this paper, we points out the problem that the high dimension image Zernike moments shape feature vector can describe more detail of the original image but has too many elements making trouble for the next image analysis phases. Then the low dimension image Zernike moments shape feature vector should be improved and optimized to describe more detail of the original image. So the optimization algorithm based on evolutionary computation is designed and implemented in this paper to solve this problem. The experimental results demonstrate the feasibility of the optimization algorithm.
文摘Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing. The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.
文摘为了简单有效地提取图像重要特征信息,从而更好地提高检索图像的精度,提出了一种基于脉冲耦合神经网络(Pulse coupled neural networks,PCNN)的图像归一化转动惯量(Normalized moment of inertia,NMI)特征提取及检索算法.首先利用改进简化PCNN模型相似神经元同步时空特性及指数衰降机制将图像分解为具有相关性的二值系列图像,然后提取反映原始图像目标形状、结构分布二值系列图像的一维NMI特征矢量信号,并将其应用在图像检索中;同时,考虑到二值系列图像间的相关性及不同图像间NMI序列值的差异性,引入了马氏距离结合Pearson积矩相关法的综合相似性度量方法.实验结果表明,所提算法对图像特征矢量序列具有良好抗几何畸变不变特性及对图像表述的唯一性,且具有较好的图像检索效果.