Study on a high-level processing for large size image using a effective geometrical graphic data structure

利用有效的几何图形数据结构对大尺寸图像进行高级处理的研究

基本信息

批准号：
61460233
负责人：
OHSAWA Yutaka
金额：
$ 3.58万
依托单位：
University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1986
资助国家：
日本
起止时间：
1986 至 1987
项目状态：
已结题

项目摘要

A new type of software-based image processing technique is proposed and investigated in this paper, which can realize very high efficiency and intelligence especially for large size (or large volume)image data such as various types of drawings and color images.In the first stage of the project, an efficient general purpose data structure suitable for 2 of 3 dimensional graphical primitives (lines, areas, volumes etc.),named the BD tree(graphic),has been introduced by devised extension of a multi-dimensional data structure BD tree which was proposed uniquely by this research group in 1983. Effectiveness of this graphical data structure has been revealed from the viewpoint of memory usage and retrieval efficiency through many computer simulations using a various types of real map data.In the next stage, a general framework for software-based large image processing has been proposed and evaluated. Large image data, typically 8000 by 6000 pixels coded by run-length method, are first conver … More ted into appropriate graphical primitives such as contour vectors by our unique method, which are then inserted bynamically to the BD tree(graphics) as preparation for hereafter processing. All the required image processing functions can be performed by a efficient and intelligent geometric operations on the graphical primitives representing the original image. Abstraction level for original graphical primitives are gradually enhanced through core vector level, higher graphical primitive level such as circle, and finally semantically recognized level. A drawing processor, the AI-MUDAMS is implemented using portable language C as a concrete embodiment of this general frame-work.In final, a new color selection technique using a similar framework using the extended threedimensional BD tree has been also proposed. Many experiments, including 16-color 256-dolor selection from 16 million color images in the Japanese Academic standard image database, SIDBA, reveals the superiority of the proposed method to other existing methods. Less

在本文中提出和研究了一种新型的基于软件的图像处理技术，该技术可以实现非常高的效率和智能，尤其是针对大尺寸（或大容量）图像数据，例如各种类型的图纸和彩色图像。该研究小组在1983年唯一提出的多维数据结构BD树。从记忆使用情况和通过许多计算机模拟的观点的角度来揭示了该图形数据结构的有效性。在下一阶段，已经提出了基于软件的大型图像处理的一般框架。首先将大型图像数据（通常由运行长度方法编码的8000 x 6000像素编码）转换为……更多的图形原始图，例如通过我们的唯一方法将其插入轮廓矢量，然后将其借助地插入BD树（图形）（图形）作为准备后处理的准备。所有必需的图像处理功能都可以通过图形原始图上的有效且智能的几何操作执行，代表原始图像。原始图形原始图的抽象水平通过核心矢量水平逐渐增强，较高的图形原始水平（例如圆），最后是语义识别的水平。绘图处理器AI-MUDAMS是使用便携式语言C作为该通用框架的具体实施例实现的。在最终，还建议使用类似的框架使用扩展的三阶BD树，这是一种新的颜色选择技术。许多实验，包括日本学术标准图像数据库中的1600万彩色图像的16色256种选择，揭示了所提出的方法与其他现有方法的超级性。较少的