RIA: Filtered Fractal Analysis for Intelligent Systems

本研究的目标是开发新的技术， 分析滤波后的离散自相似信号，并研究其 在语音建模与识别、图像纹理等方面的应用 分割和基于轮廓信息的目标识别。 具体地说，滤波离散自相似信号被用作工具 为模拟的长期和短期的特点， 感兴趣的信号和新的多尺度算法， 参数已建立。 问题的多尺度解 找到一个迭代函数系统，其吸引子最佳逼近 也正在开发Hausdorff测度中的给定M-D曲线。 将特别注意同时执行 算法 滤波离散自相似信号也被研究作为模型， 特别强调语音信号的自然性， 制作演讲。 此外，它们的参数和 迭代函数系统对语音半音节的近似是 测试用作语音识别系统中的特征向量。 M-D 实验也正在进行调查的有用性 M-D滤波离散自相似信号与迭代函数系统 纹理分割和物体识别任务。

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.