Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of morphing beten two polylines that represent linear geographical features like roads or rivers generalized at two different scales. This problem occurs frequently during continuous zooming in interactive maps. Situations in which generalization operators like typification and simplification replace, for example, a series of consecutive bends by fer bends are not always handled ll by traditional morphing algorithms.  attempt to cope with such cases by modeling the problem as an optimal correspondence problem beten characteristic parts of each polyline. A dynamic programming algorithm is presented that solves the matching problem in O(nm) time, where n and m are the respective numbers of characteristic parts of the two polylines. In a case study  demonstrate that the algorithm yields good results when being applied to data from mountain roads, a river and a region boundary at various scales.
['Martin Nöllenburg', 'Damian Merrick', 'Alexander Wolff', 'Marc Benkert']
I am writing a research paper on a idea Morphing polylines: A step towards continuous generalization
[{"from": "human", "value": "I am writing a research paper on a idea Morphing polylines: A step towards continuous generalization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  study the problem of morphing beten two polylines that represent linear geographical features like roads or rivers generalized at two different scales. This problem occurs frequently during continuous zooming in interactive maps. Situations in which generalization operators like typification and simplification replace, for example, a series of consecutive bends by fer bends are not always handled ll by traditional morphing algorithms.  attempt to cope with such cases by modeling the problem as an optimal correspondence problem beten characteristic parts of each polyline. A dynamic programming algorithm is presented that solves the matching problem in O(nm) time, where n and m are the respective numbers of characteristic parts of the two polylines. In a case study  demonstrate that the algorithm yields good results when being applied to data from mountain roads, a river and a region boundary at various scales."}]