2D-2-3D automated conversion of two-dimensional contour maps into three-dimensional graphic images - Chung, Stanford Sy (1999)
Digitizing maps would answer the many problems that geologist encounter such as the difficulty in representing visually, areas depicted in contour map and the storage, retrieval, and manipulation of maps. Current techniques in digitizing maps are available but they are not that efficient. A computer-aided design software and digitizer, makes it possible to generate digitized images of contour maps. However, this requires expertise and huge amount of time and effort. An alternative to digitizing contour maps is to scan the map. The problem however with scanned images is that they are useless to data driven applications because they are only image files. Another alternative is to use current software that could generate three-dimensional images of contour maps by supplying raw data of coordinates. But then again, this requires a lot of time and effort.In this paper, a new system is proposed which automatically converts a scanned two-dimensional contour map into a three-dimensional graphic image. It aims to solve the problems encountered in digitizing contour maps, thus lessening the time and effort used in current techniques. 2D23D is a software capable of handling scanned images of contour maps in bitmap format and automatically converts this into a well-defined three-dimensional graphic image. 2D23D is divided into three modules. The first is the map instruction module. Here image processing must be performed such as edge detection, edge or contour following, and self-organizing map (SOM) to get the necessary values for constructing the three dimensional graphic image. The second is the stitching module. Maps that require several files to store would have to be stitched together. This will be done by using the unique characteristics of the contours for the knowledge for its specific position on the map or to have special icons in the map serve as markers for a specific position or coordinates and then match the special markers. Stitching starts when an exact match for the markers for each part of the map is found. Last is the optical character recognition module. It deals with the recognition of alphanumeric characters located within the map. It will implement classification of recognized characters and after interpretation, will be used for the generation of 3D image. The methods used for character recognition are template matching and feature analysis.