A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Algorithm for Point Set Registration Using Mixture of Gaussians
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Efficient integration of road maps
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
Automatically Conflating Road Vector Data with Orthoimagery
Geoinformatica
Automatic alignment of large-scale aerial rasters to road-maps
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Automated geospatial conflation of vector road maps to high resolution imagery
IEEE Transactions on Image Processing
A comparative study of transformation functions for nonrigid image registration
IEEE Transactions on Image Processing
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This paper presents an automatic approach to rectify misalignments between a geo-referenced Very High Resolution (VHR) optical image (raster) and a road database (vector). Due to inconsistent representations of road objects in different data sources, the extraction and validation of the homologous road features are complicated. The proposed Sparse Matching (SM) approach is able to smoothly snap the road features from the vector database to their corresponding road features in the VHR image. This novel conflation approach includes three main steps: linear feature preprocessing; sparse matching; feature transformation. Instead of directly extracting the complete road network from the image, which is still a challenging topic for the image processing community, the linear features as road candidates are extracted using Elastic Circular Mask (ECM) and the existing noises are filtered by means of perceptual factors via Genetic Algorithm (GA). With the sparse matching approach, the correspondence between the road candidates from the image and the road features from the vector database can be maximized in terms of geometric and radiometric characteristics. Finally, we compare the transformation results from two different transformational functions i.e. the piecewise Rubber-Sheeting (RUBS) approach and the Thin Plate Splines (TPS) approach for the matched features. The main contributions of this proposed approach include: 1) A novel sparse matching approach especially for conflation framework; 2) Efficient noise filtering in the results from the ECM detector and the GA approach; 3) Numerical comparison of two popular transformational functions. The proposed method has been tested for variant imagery scenario and over 80 percent correct ratio can be achieved from our experiment, at the same time, the average Root Mean Square (RMS) value decreases from 30 meter to less than 10 meter, which makes it possible to use snake-based algorithm for further process.