Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Perceptual grouping for symbol chain tracking in digitized topographic maps
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Line and Geographic Feature Extraction from USGS Color Topographical Paper Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge-Based Segmentation for Automatic Map Interpretation
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
Segmentation of Thematic Maps Using Colour and Spatial Attributes
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Symbol Recognition: Current Advances and Perspectives
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Extracting Contour Lines from Scanned Topographic Maps
CGIV '04 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Grayscale Image Segmentation Using Color Space
IEICE - Transactions on Information and Systems
A color image segmentation approach for content-based image retrieval
Pattern Recognition
Color image segmentation by analysis of subset connectedness and color homogeneity properties
Computer Vision and Image Understanding
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Automatic data capture for geographic information systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Extracting road vector data from raster maps
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Segmentation of colour layers in historical maps based on hierarchical colour sampling
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Efficient and robust graphics recognition from historical maps
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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A novel approach to color image segmentation (CIS) in scanned archival topographic maps of the 19th century is presented. Archival maps provide unique information for GIS-based change detection and are the only spatially contiguous data sources prior to the establishment of remote sensing. Processing such documents is challenging due to their very low graphical quality caused by ageing, manual production and scanning. Typical artifacts are high degrees of mixed and false coloring, as well as blurring in the images. Existing approaches for segmentation in cartographic documents are normally presented using well-conditioned maps. The CIS approach presented here uses information from the local image plane, the frequency domain and color space. As a first step, iterative clustering is based on local homogeneity, frequency of homogeneity-tested pixels and similarity. By defining a peak-finding rule, "hidden" color layer prototypes can be identified without prior knowledge. Based on these prototypes a constrained seeded region growing (SRG) process is carried out to find connected regions of color layers using color similarity and spatial connectivity. The method was tested on map pages with different graphical properties with robust results as derived from an accuracy assessment.