A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
Segmentation of physiographic features from the global digital elevation model/GTOPO30
Computers & Geosciences
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Parallel image restoration on parallel and distributed computers
Parallel Computing
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Parallel domain decomposition procedures of improved D-D type for parabolic problems
Journal of Computational and Applied Mathematics
Force field analysis snake: an improved parametric active contour model
Pattern Recognition Letters
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
Parallel contributing area calculation with granularity control on massive grid terrain datasets
Computers & Geosciences
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Loess shoulder-lines are the most critical terrain feature in representing and modeling the landforms of the Loess Plateau of China. Existing algorithms usually fail in obtaining a continuous shoulder-line for complicated surface, DEM quality and algorithm limitation. This paper proposes a new method, by which gradient vector flow (GVF) snake model is employed to generate an integrated contour which could connect the discontinuous fragments of shoulder-line. Moreover, a new criterion for the selection of initial seeds is created for the snake model, which takes the value of median smoothing of the local neighborhood regions. By doing this, we can extract the adjacent boundary of loess positive-negative terrains from the shoulder-line zones, which build a basis to found the real shoulder-lines by the gradient vector flow. However, the computational burden of this method remains heavy for large DEM dataset. In this study, a parallel computing scheme of the cluster for automatic shoulder-line extraction is proposed and implemented with a parallel GVF snake model. After analyzing the principle of the method, the paper develops an effective parallel algorithm integrating both single program multiple data (SPMD) and master/slave (M/S) programming modes. Based on domain decomposition of DEM data, each partition is decomposed regularly and calculated simultaneously. The experimental results on different DEM datasets indicate that parallel programming can achieve the main objective of distinctly reducing execution time without losing accuracy compared with the sequential model. The hybrid algorithm in this study achieves a mean shoulder-line offset of 15.8m, a quite satisfied result in both accuracy and efficiency compared with published extraction methods.