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

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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