Scale-space filtering using a piecewise polynomial representation
Scale-space filtering using a piecewise polynomial representation
复制标题
使用分段多项式表示的尺度空间过滤
DOI：
10.1109/icip.2014.7025590
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
K. Uchimura
中科院分区：
文献类型：
--
作者：
G. Koutaki;K. Uchimura
尺度空间图像处理是计算机视觉中用于目标识别和低层特征提取的基本技术。已经提出了许多高斯滤波技术。最近，谱分解方法被提出，这是主成分分析的无限版本。利用该方法，高斯模糊图像可以表示为一个尺度参数的多项式和高斯模糊图像可以得到一个任意尺度从简单的线性组合的卷积特征图像。然而，在该方法中，尺度被限制在小范围内。在这项研究中，我们提出了一个改进的高斯核的谱分解扩大规模使用分段多项式表示。本文分析了高斯核的连续谱分解及其本征解。实验结果表明，该方法可以生成任意尺度、宽尺度范围的高精度高斯模糊图像。
Scale-space image processing is a basic technique used for object recognition and low-level feature extraction in computer vision. Many Gaussian filtering techniques have been proposed. Recently, the spectral decomposition method was proposed, which is an infinite version of principal components analysis. Using this method, Gaussian blurred images can be represented as polynomials with a scale parameter and a Gaussian blurred image with an arbitrary scale can be obtained from simple linear combinations of the convolved eigenimages. However, the scale is limited to a small range in this method. In this study, we propose an improvement to the spectral decomposition of a Gaussian kernel by widening the scale using a piecewise polynomial representation. We present an analysis of the continuous spectral decompositions of a Gaussian kernel and their eigensolutions. Experimental results show that the proposed method can generate accurate Gaussian blurred images with an arbitrary scale and a wide scale range.