A randomized linear-time algorithm to find minimum spanning trees
Journal of the ACM (JACM)
Multiscale Nonlinear Decomposition: The Sieve Decomposition Theorem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
A Comparison of Algorithms for Connected Set Openings and Closings
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient algorithm for constructing Hamiltonian paths in meshes
Parallel Computing
Outex - New Framework for Empirical Evaluation of Texture Analysis Algorithms
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
Colour Morphological Scale-Spaces from the Positional Colour Sieve
DICTA '05 Proceedings of the Digital Image Computing on Techniques and Applications
Mathematical Morphology in Any Color Space
ICIAPW '07 Proceedings of the 14th International Conference of Image Analysis and Processing - Workshops
IEEE Transactions on Image Processing
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The sieve is a morphological scale-space operator that filters an input signal by removing intensity extrema at a specific scale. In images, this processing can be carried out along a path - the 1D sieve - or over a connected graph - the 2D sieve. The 2D version of the sieve generally performs better; it is however much more complex to implement. In this paper we present the 1.5D sieve, a Hamiltonian path-based version of the sieve algorithm that behaves ''in between'' the 1D or 2D sieve algorithms, depending on the number of paths used. Experiments show that its robustness to the presence of noise and its performance in texture classification are similar to the original 2D sieve formulation, while being much faster and simpler to implement.