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This paper presents a new method for use in performing continuous scale transformations of linear features using Simulated Annealing-Based Morphing (SABM). This study addresses two key problems in the continuous generalization of linear features by morphing, specifically the detection of characteristic points and correspondence matching. First, an algorithm that performs robust detection of characteristic points is developed that is based on the Constrained Delaunay Triangulation (CDT) model. Then, an optimal problem is defined and solved to associate the characteristic points between a coarser representation and a finer representation. The algorithm decomposes the input shapes into several pairs of corresponding segments and uses the simulated annealing algorithm to find the optimal matching. Simple straight-line trajectories are used to define the movements between corresponding points. The experimental results show that the SABM method can be used for continuous generalization and generates smooth, natural and visually pleasing linear features with gradient effects. In contrast to linear interpolation, the SABM method uses the simulated annealing technique to optimize the correspondence between characteristic points. Moreover, it avoids interior distortions within intermediate shapes and preserves the geographical characteristics of the input shapes.

Continuous Scale Transformations of Linear Features Using Simulated Annealing-Based Morphing