Partially observed objects localization with PCA and KPCA models

B. Romaniuk, V. Guilloux, M. Desvignes, M. Deshayes
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引用次数: 1

Abstract

We deal with the problem of partially observed objects. These objects are defined by sets of points and their shape variations are represented by a statistical model. We present two models: a linear model based on PCA and a non-linear model based on KPCA (kernel PCA). The present work attempts to localize non visible parts of an object from visible parts and from the model, explicitly. using the variability represented by the model. Both are applied to the cephalometric problem with good results.
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利用PCA和KPCA模型对部分观测对象进行定位
我们处理部分观察到的物体的问题。这些对象由一组点定义,它们的形状变化由统计模型表示。我们提出了两种模型:基于主成分分析的线性模型和基于核主成分分析的非线性模型。目前的工作试图明确地从可见部分和模型中定位对象的不可见部分。利用模型所表示的可变性。这两种方法都适用于头位测量问题,效果良好。
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Color interpolation for single CCD color camera A spatially selective filter based on the undecimated wavelet transform that is robust to noise estimation error Partially observed objects localization with PCA and KPCA models Multi-resolution volumetric reconstruction using labeled regions Frequency implementation of discrete wavelet transforms
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