Analytic Sciences Corporation, Reading, MA 01867.
IEEE Trans Pattern Anal Mach Intell. 1987 Jan;9(1):121-9. doi: 10.1109/tpami.1987.4767877.
A new application of scale-space filtering to the classical problem of estimating the parameters of a normal mixture distribution is described. The technique involves generating a multiscale description of a histogram by convolving it with a series of Gaussians of gradually increasing width (standard deviation), and marking the location and direction of the sign change of zero-crossings in the second derivative. The resulting description, or fingerprint, is interpreted by relating pairs of zero-crossings to modes in the histogram where each mode or component is modeled by a normal distribution. Zero-crossings provide information from which estimates of the mixture parameters are computed. These initial estimates are subsequently refined using an iterative maximum likelihood estimation technique. Varying the scale or resolution of the analysis allows the number of components used in approximating the histogram to be controlled.
描述了一种将尺度空间滤波应用于经典的正态混合分布参数估计问题的新方法。该技术通过用一系列宽度（标准差）逐渐增大的高斯函数对直方图进行卷积，生成一个多尺度描述，并标记二阶导数中过零点的符号变化的位置和方向。所得描述（或指纹）通过将过零点与直方图中的模式相关联来解释，其中每个模式或分量由正态分布建模。过零点提供了用于计算混合参数估计值的信息。这些初始估计值随后使用迭代最大似然估计技术进行细化。改变分析的尺度或分辨率可以控制用于近似直方图的分量数量。
