Extraction of ROI in Geographical Map » książka
Extraction of ROI in Geographical Map
ISBN-13: 9783639358148 / Angielski / Miękka / 2011 / 72 str.
Region of Interest (ROI) extraction from geographical map image is an important task of document analysis and recognition. The extracted segments are applied to different machine vision and embedded system. The task is very complex because of having overlapping objects, more types of data, possible curvature, even branching of graphics, intersected lines etc in map. Keeping this in mind, this book describes two methods that have been applied to extract efficient ROI for both road network and waterway from geographical map; one is color based segmentation applying K-means clustering and other is template based matching which overcome the previous limitations. Different from the existent methods, these proposed approaches are efficient both in segmentation results and further reconstruction also. And the experimental results are close to human perceptions; therefore these methods provide better and more robust performance than either of the individual methods.
Region of Interest (ROI) extraction from geographical map image is an important task of document analysis and recognition. The extracted segments are applied to different machine vision and embedded system. The task is very complex because of having overlapping objects, more types of data, possible curvature, even branching of graphics, intersected lines etc in map. Keeping this in mind, this book describes two methods that have been applied to extract efficient ROI for both road network and waterway from geographical map; one is color based segmentation applying K-means clustering and other is template based matching which overcome the previous limitations. Different from the existent methods, these proposed approaches are efficient both in segmentation results and further reconstruction also. And the experimental results are close to human perceptions; therefore these methods provide better and more robust performance than either of the individual methods.
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