RIA: Filtered Fractal Analysis for Intelligent Systems

本研究的目标是开发新的分析滤波离散自相似信号的技术，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目标识别中的应用。具体地说，利用经过滤波的离散自相似信号作为建模工具，对感兴趣信号的长期和短期特性进行建模，并构造了新的多尺度算法来估计其参数。寻找其吸引子在Hausdorff测度下最佳逼近给定M-D曲线的迭代函数系统的多尺度解决方案也在开发中。将特别注意算法的并行实现。还研究了经过滤波的离散自相似信号作为语音信号的模型，特别强调所产生的语音的自然度。此外，测试了它们的参数和迭代函数系统对语音去音节的逼近的参数，以用于语音识别系统中的特征向量。M-D实验也被用来研究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.