RIA: Filtered Fractal Analysis for Intelligent Systems

本研究的目标是开发新的滤波离散自相似信号分析技术，并研究其在语音建模和识别、图像纹理分割和基于轮廓信息的物体识别中的应用。具体来说，滤波后的离散自相似信号被用作对感兴趣信号的长期和短期特征建模的工具，并构建了用于估计其参数的新型多尺度算法。寻找一个迭代函数系统的问题的多尺度解，其吸引子在豪斯多夫测度中最接近给定的M-D曲线，也正在开发。将特别注意算法的并行实现。滤波后的离散自相似信号也被研究作为语音信号的模型，特别强调所产生语音的自然度。此外，测试了它们的参数和迭代函数系统逼近语音半音节的参数，以作为语音识别系统中的特征向量。M-D实验也正在进行，以研究M-D滤波的离散自相似信号和迭代函数系统在纹理分割和目标识别任务中的有用性。

The goal of this research is to develop novel techniques for the analysis of filtered discrete self-similar signals and investigate their application in speech modeling and recognition, image texture segmentation and object recognition based on contour information. Specifically, filtered discrete self-similar signals are used as tools for modeling both the long term and short term characteristics of signals of interest and novel multiscale algorithms for estimating their parameters are constructed. A multiscale solution to the problem of finding an iterated function system whose attractor best approximates a given M-D curve in the Hausdorff measure is also being developed. Particular attention will be given to parallel implementation of the algorithms. Filtered discrete self-similar signals are also studied as models for speech signals with particular emphasis on the naturalness of the produced speech. Furthermore, their parameters and the parameters of iterated function systems approximations to speech demisyllables are tested for use as feature vectors in speech recognition systems. M-D experiments are also being performed to investigate the usefulness of M-D filtered discrete self-similar signals and iterated function systems in texture segmentation and object recognition tasks.