共查询到18条相似文献,搜索用时 703 毫秒
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图像矢量量化—频率敏感自组织特征映射算法 总被引:17,自引:0,他引:17
用神经网络实现图像矢量量化是一种非常有效的方法,本文在分析自组织特征映射(SOFM)算法的基础上,提出了一种频率敏感自组织特征映射(FSOFM)算法,并对网络学习训练参数的优化进行了探讨。实验表明,FSOFM算法优于SOFM算法。 相似文献
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图像矢量量化(VQ)是图像压缩算法中的重要环节,在VQ中起决定性因素的是构造出性能优异的码书。为改善矢量量化码书的性能,文中在分析Kohonen自组织特征映射(SOFM)的基础上,提出一种识别距离SOFM的算法,同时将矢量量化应用于图像的小波变换域。测试结果表明,改进的算法使码书设计的计算量得到明显的降低,而且码书的性能得到了提高。 相似文献
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改进的Kohonen网络及图像自适应矢量量化 总被引:6,自引:2,他引:4
本文针对图像矢量量化存在的分块效应问题,通过对Kohonen自组织模型的研究,修改了Kohonen的自组织特征映射(SOFM)算法,设计了两个DCT(离散余弦变换)域的特征值,用于图像数据块的分类。在此基础上,进一步探讨了改进的自组织特征映射(MSOFM)算法在图像自适应矢量量化中的应用。计算机模拟实验表明,MSOFM算法有效地减少了分块效应,与SOFM算法相比具有更好的性能。 相似文献
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一种用于图象编码的神经网络及其改进算法 总被引:2,自引:0,他引:2
本文用Kohonen的自组织特征映射神经网络设计图象矢量量化的码书,研究了网络的基本性质和学习算法的实现,提出了对学习算法的改进方法。实验结果表明,自组织特征映射神经网络能够有效地用于构造图象矢量量化的码书,算法简洁,实现快速,编码效果优良。 相似文献
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基于核自组织映射与图论的图像分割方法 总被引:1,自引:1,他引:0
为了对以特征聚类为基础的图像分割方法进行目标优化并提高分割性能,提出了一种核自组织映射与EGB(efficient graph-based)算法相结合的自适应分割方法。将依据信息理论推导出的核自组织映射应用于图像分割,使得图像经映射聚类后,同一分类内像素的相似度最高且信息熵最大,不同分类间的互信息最小,从而得到最符合图像分割目标的聚类效果。将聚类得到的区域进一步用改进的EGB算法自适应地进行合并,既充分结合了像素的空间特性,又能克服EGB算法的不足,可获得非常准确的分割结果。在综合分析多种图像分割评价方法的基础上,选取了一些量化指标对分割结果进行客观评价。实验及分析结果表明,本文的分割方法准确可靠,其图像分割结果的量化评价指标明显优于EDISON方法。 相似文献
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该文提出了一种基于双正交小波变换(BWT)和模糊矢量量化(FVQ)的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。该算法采用非线性插补矢量量化(NLIVQ)的思想,从大维数矢量中提取小维数的特征矢量,并提出了一种新的模糊矢量量化方法一渐进构造模糊聚类(PCFC)算法用于特征矢量的量化,从而大大提高了矢量量化的速度和码书质量。实验结果证明,该算法在比特率为0.172bpp的条件下仍能获得PSNR>30dB的高质量重构图像。 相似文献
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针对目前视频编码中广泛采用的块匹配运动估计补偿(ME+MC)算法的不足,提出一种基于自组织映射(SOM)的运动模式识别(MPR)算法,并将其应用于会议电视的视频对象编码中.为了改善SOM算法的性能,提出一种频率敏感的自组织映射算法(FSOM).实验表明,与ME+MC算法相比,FSOM-MPR算法具有更好的预测编码性能.对Claire视频测试序列,当压缩比为170∶1时,重建视频图像的平均峰值信噪比(PSNR)有2.7dB的改善. 相似文献
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一种基于自组织神经网络的图像压缩缩码算法 总被引:8,自引:0,他引:8
本文提出了一种基于自组织特征映射神经网络的图像压缩编码算法,即VQ+DPCM+DCT算法,实验表明,在压缩比为31.8:1时,其峰峰信噪比为35.82dB且主观效果良好,这时至今为止使用矢量量化方法压缩图像所获得的最好结果。 相似文献
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A new learning scheme, called projection learning (PL), for self-organizing neural networks is presented. By iteratively subtracting out the projection of the “twinning” neuron onto the null space of the input vector, the neuron is made more similar to the input. By subtracting the projection onto the null space as opposed to making the weight vector directly aligned to the input, we attempt to reduce the bias of the weight vectors. This reduced bias will improve the generalizing abilities of the network. Such a feature is important in problems where the in-class variance is very high, such as, traffic sign recognition problems. Comparisons of PL with standard Kohonen learning indicate that projection learning is faster. Projection learning is implemented on a new self-organizing neural network model called the reconfigurable neural network (RNN). The RNN is designed to incorporate new patterns online without retraining the network. The RNN is used to recognize traffic signs for a mobile robot navigation system 相似文献
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电子封装常用名称及术语汇集下面,按英文字母顺序,汇集并解释了与目前LSI(包括IC)正在采用的主要封装形式相关联的名称术语等。这些名称术语参考并引用了日本国内12个半导体制造公司,其他国家7个半导体制造公司*与LSI封装相关的资料、日本电子机械工业会... 相似文献
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We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization. 相似文献
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Soo-Chang Pei Yu-Ting Chuang Wei-Hong Chuang 《IEEE transactions on image processing》2006,15(9):2493-2498
The process of limited-color image compression usually involves color quantization followed by palette re-indexing. Palette re-indexing could improve the compression of color-indexed images, but it is still complicated and consumes extra time. Making use of the topology-preserving property of self-organizing Kohonen feature map, we can generate a fairly good color index table to achieve both high image quality and high compression, without re-indexing. Promising experiment results will be presented. 相似文献
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The self-organizing map 总被引:27,自引:0,他引:27
Kohonen T. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1990,78(9):1464-1480
The self-organized map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications. The self-organizing map has the property of effectively creating spatially organized internal representations of various features of input signals and their abstractions. One result of this is that the self-organization process can discover semantic relationships in sentences. Brain maps, semantic maps, and early work on competitive learning are reviewed. The self-organizing map algorithm (an algorithm which order responses spatially) is reviewed, focusing on best matching cell selection and adaptation of the weight vectors. Suggestions for applying the self-organizing map algorithm, demonstrations of the ordering process, and an example of hierarchical clustering of data are presented. Fine tuning the map by learning vector quantization is addressed. The use of self-organized maps in practical speech recognition and a simulation experiment on semantic mapping are discussed 相似文献
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The self-organizing map (SOM) is an unsupervised learning neural network. The goal of this network is to produce a similarity graph of input data. It is a two-dimensional mesh of neurons each with a weight vector. Generally, the network organizes the nodes in the grid into local neighborhoods and the learning process acts as feature classifiers on the input data. The feature map is formed automatically through a cyclic process by comparing input patterns to vectors at each node. No training … 相似文献