Symmetry Maps of Free-Form Curve Segments via Wave Propagation
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
The "Dead reckoning" signed distance transform
Computer Vision and Image Understanding
Edge affinity for pose-contour matching
Computer Vision and Image Understanding
Out-of-core distance transforms
Proceedings of the 2007 ACM symposium on Solid and physical modeling
Computer Vision, Image Analysis, and Master Art: Part 3
IEEE MultiMedia
Hierarchical contour matching for dental X-ray radiographs
Pattern Recognition
2D Euclidean distance transform algorithms: A comparative survey
ACM Computing Surveys (CSUR)
Object segmentation within microscope images of palynofacies
Computers & Geosciences
Errors in river lengths derived from raster digital elevation models
Computers & Geosciences
Traffic sign recognition using discriminative local features
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Sparse grid distance transforms
Graphical Models
An efficient euclidean distance transform
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Accelerating the distance transform
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Extension of a GIS procedure for calculating the RUSLE equation LS factor
Computers & Geosciences
Approximation of the euclidean distance by Chamfer distances
Acta Cybernetica
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The distance transform has found many applications in image analysis. Chamfer distance transforms are a class of discrete algorithms that offer a good approximation to the desired Euclidean distance transform at a lower computational cost. They can also give integer-valued distances that are more suitable for several digital image processing tasks. The local distances used to compute a chamfer distance transform are selected to minimize an approximation error. A new geometric approach is developed to find optimal local distances. This new approach is easier to visualize than the approaches found in previous work, and can be easily extended to chamfer metrics that use large neighborhoods. A new concept of critical local distances is presented which reduces the computational complexity of the chamfer distance transform without increasing the maximum approximation error