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Fuzzy objects based image compression system for managing vast images

用于管理大量图像的基于模糊对象的图像压缩系统

基本信息

批准号：
15300069
负责人：
HIROTA Kaoru
金额：
$ 10.37万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

项目摘要

A fuzzy objects based image compression system is proposed. As an image compression system based on emerging computational resources in order to manage vast images. The proposed system is composed of four parts, 1. pre-processing part, 2. objects extraction part, 3. fuzzy objects database, and 4 retrieval part.1. Pre-processing part : Two multi-resolution transformations are proposed based on different mathematical structures. The first one is an image compression and reconstruction method based on fuzzy relational equations (fuzzy algebra), and the second one is morphological wavelets based on max plus algebra.2. Object extraction part : The original images are decomposed into fuzzy objects by using a fuzzy relation decomposition method proposed in this research. Furthermore, an optical flow based segmentation method is also proposed to decompose the original images.3. Fuzzy object database part: With respect to the fuzzy objects, an optimized database structure is constructed by using the proposed tree construction algorithm along with pai-t neural networks.4. Retrieval part : In order to reduce the retrieval time and improve the quality of the reconstructed image obtained by the proposed system, a color instance based retrieval method is proposed.Through the evaluation experiments using various images (selected from standard image database, Corel gallery, and obtained by surveillance camera), the effectiveness of the proposed image compression system is confirmed.

提出了一种基于模糊对象的图像压缩系统。作为一种基于新兴计算资源的图像压缩系统，以管理大量图像。该系统由四个部分组成：1.预处理部分，2.对象提取部分，3.模糊对象数据库，4.检索部分。预处理部分：基于不同的数学结构提出了两种多分辨率变换。第一个是基于模糊关系方程（模糊代数）的图像压缩与重建方法，第二个是基于最大加代数的形态小波。 2．对象提取部分：利用本研究提出的模糊关系分解方法将原始图像分解为模糊对象。此外，还提出了一种基于光流的分割方法来分解原始图像。 3．模糊对象数据库部分：针对模糊对象，利用所提出的树构建算法和pai-t神经网络构建优化的数据库结构。 4．检索部分：为了减少检索时间并提高该系统获得的重建图像的质量，提出了一种基于颜色实例的检索方法。通过使用各种图像（选自标准图像数据库、Corel画廊和监控摄像机获得的图像）进行评估实验，证实了所提出的图像压缩系统的有效性。