Achieving application requirements
Distributed systems
Erosion and Dilation on 2-D and 3-D Digital Images: A New Size-Independent Approach
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
A widget framework for augmented interaction in SCAPE
Proceedings of the 16th annual ACM symposium on User interface software and technology
A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions
Journal of Systems and Software - Special issue: Quality software
Parallel Asynchronous Watershed Algorithm-Architecture
IEEE Transactions on Parallel and Distributed Systems
Watershed segmentation using prior shape and appearance knowledge
Image and Vision Computing
Method of DEM Data's Processing in Flood Simulation System
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 06
Pattern Recognition Letters
Hi-index | 0.00 |
DEM (Digital Elevation Model) is the model that gives elevation of each point of the earth surface in discrete form in a 3-D space. Image registration, implies registration of multi-temporal, multi-modal, multi-resolution, images of the same area. Registration of DEMs is now a days gaining a lot of popularity among the research community. DEM registration, in this case, is the registration of multi-temporal DEMs. Popular techniques for feature extraction and matching include wavelet approach, robust SIFT, or are based "super-points". This paper presents the DEM registration scheme based on watershed transformation, followed by two post-processing steps of clustering and morphological operations which is applied on both the DEMs -- candidate, as well as, reference DEM. Chain coding based matching is concluding step of the complete process. The system is semi-automatic i. e. expert input is considered before the final registration. Experimental results give good outcomes as shown from the error matrix analysis of RMSE, and PSNR. The system may be extended by using fuzzy classification and context-sensitive learning.