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于 2014-08-13 发布文件大小:120KB

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Today, there is almost no area of technical endeavor that is not impacted in some way by digital image processing.We can cover only a few of these applications in the context and space of the current discussion. However, limited as it is, the material presented in this section will leave no doubt in the reader’s mind regarding the breadth and importance of digital image processing.We show in this section numerous areas of application, each of which routinely utilizes the digital image processing techniques developed in the following chapters. Many of the images shown in this section are used later in one or more of the examples given in the book. All images shown are digital.

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sanci
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2011-03-23 21:23:42下载

积分：1
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Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

2020-09-20 10:47:53下载

积分：1
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2015-04-15 09:52:14下载

积分：1
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积分：1
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2012-05-13 21:41:13下载

积分：1

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