Perceptual grouping for symbol chain tracking in digitized topographic maps

A Method to Extract Essential Information from Meteorological Facsimile Charts

Using image processing technology to extract important information, such as isoline and weather system of the meteorological facsimile chart, is conducive to integration with other information, and has important practical value in navigation operations, marine weather forecasting, target recognition, and image retrieval. In meteorological facsimile charts, there are many types of medium-value lines, dense lines in some areas, superimposition and presence of multiple information, such as isolines and isoline characters, intersection of specific weather system symbols, etc. For different types of contours, numeric characters, weather system symbols and other object characteristics, the corresponding object extraction and recognition methods are proposed: Remove the latitude and longitude lines and coastline in the meteorological facsimile map by basemap matching; According to the position and shape features of the figure box, extract the meteorological fax figure box, separate and remove the different character tagging information; On the basis of identifying triangles and semicircles in weather symbols of the frontal system, the frontal symbols are extracted based on the circumscribed triangles and template matching. First the contour character on the fax image is expanded into a block connected region. Determine the position of the character information by judging the number of pixels in the connected region, and then use rotation and template matching to identify the numeric character. Using the meteorological facsimile maps of the US Meteorological Center and the Japan Meteorological Center for the main information extraction, experiments show that the method of this paper has a good effect on the complete and accurate symbol extraction of frontal weather systems, and reduces the computational complexity of contour detection, isoline extraction and numerical recognition. The methods can detect some information from weather charts properly and the error rate is very low.