Intelligent Recognition Of Line In Scanned Color Maps Based On Segment

Geographic Information Acquisition (GIA) which accounts for 80% of the whole work is important for Geographical Information System (GIS) development, and still a bottleneck of the development for the GIS. Automatic interpretation and acquisition of topographic map is the core of GIA because it involves many subjects such as image processing, pattern recognition and artificial intelligence, etc. At the same time, it is one of key issues in the field of computer vision.The research on recognition is important in both theory and practical application. Previous theories and applications have provided a solid foundation for further research, but there are still some urgent and thorny problems. New approaches of recognition have to be proposed.The topographic map, which can be regarded as vector line graph, is a set of point, line and surface symbols, while the scanned map is composed of pixels. To extract symbols from pixels, recognition process should be self-organizationally implemented by different levels. The approach developed by this dissertation aims to capture more global features. The constraints of the segment are extended to meet the color, width, and topological consistency, so that the segment is the expression unit of a single meaning for image. Firstly, according to the relationship between pixels, images are run-length encoded and the scan strings with the attribute are built. Secondly, based on the color, width and topological consistency of scan strings, the segments expressing image are obtained. At the same time, connecting relationships among the segments are extracted for building the segment connected components. Thirdly, according to the properties of the components, the line symbols are extracted from point and surface symbols. The adjacency relationships between the vector line symbols are constructed to search for all line symbols belonging to the same map element, and thus we can rebuild the map elements.According to the organization of topographic map information in the human brain, this dissertation proposes a three-tier model of topographic map target:the map element model, symbolic model and image model. The symbolic model relates the realistic model and the image model. At the same time, it also has the raster data characteristic therefore we might find ways to all pixels of surface features by its identification number. Recognizing and extracting surface features through the symbolic model, we can use not only the partial characteristic of all pixels, but also the overall characteristics such as topological relationship. Data in the same level and in the different levels are associated with each other, so the associations are paid great attention horizontally and vertically. High-level information originates from low-level data and in turn instructs low-level data, therefore, the recognition is completed through bottom-up and top-down reasoning combination.To take full advantage of the color information of topographic map, the dissertation puts forward the idea to increase the number of color categories in pixel level, which effectively solved the difficult problem of fuzzy transition-color separation. In scan string level color combinations are classified into 16 sorts and are skillfully indicated with the sum of the various color code, which enables scan string to have the color information and provides the foundation for obtaining segments'color. In this paper, run-length coding technology is used to realize the conversion from pixel matrix to scan string and then to segment which makes up of the adjacent scan strings meeting the color, width, and topological consistency. The segment can directly express line and intersection. Single version of a connected graph is built using the color information and the adjacent relationship of segment, thus achieving automatic color separation of topographic map image. The method has a high processing efficiency and reduces much noise since taking into the color attribute and spatial relationship.By analyzing the shape, size and topology of the connected component, the dissertation has summarized characters for expression, such as the size, aspect ratio, black and white ratio and node density, and has effectively separated line symbol from point symbol and surface symbol based on these characteristic. The sub-vector method is adopted to deal with line symbols, at the same time, homologous lines are detected according to the adjacency relationship of node segments. Through the process, all vector lines to the same map element are obtained and the complete vector information is established. In order to better extract the vector data of dashed roads, rivers and contour lines, test conditions can be adaptively adjusted according to different map elements.The above mentioned recognition methods have been realized in the already-developed prototype system of recognizing the topographic map. In which, the software shall be designed according to the objects and data be managed by common database.