Name
Abstract | ||
|---|---|---|
In this paper we propose a variational method to adaptively decompose an image into few different modes of separate spectral bands, which are unknown before. A popular method for recursive one dimensional signal decomposition is the Empirical Mode Decomposition algorithm, introduced by Huang in the nineties. This algorithm, as well as its 2D extension, though extensively used, suffers from a lack of exact mathematical model, interpolation choice, and sensitivity to both noise and sampling. Other state-of-the-art models include synchrosqueezing, the empirical wavelet transform, and recursive variational decomposition into smooth signals and residuals. Here, we have created an entirely non-recursive 2D variational mode decomposition (2D-VMD) model, where the modes are extracted concurrently. The model looks for a number of 2D modes and their respective center frequencies, such that the bandlimited modes reproduce the input image (exactly or in least-squares sense). Preliminary results show excellent performance on both synthetic and real images. Running this algorithm on a peptide microscopy image yields accurate, timely, and autonomous segmentation - pertinent in the fields of biochemistry and nanoscience. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-14612-6_15 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Bandlimiting,Computer science,Variational method,Interpolation,Algorithm,Artificial intelligence,Real image,Spectral bands,Recursion,Wavelet transform,Hilbert–Huang transform | Conference | 8932 |
ISSN | Citations | PageRank |
0302-9743 | 4 | 0.46 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantin Dragomiretskiy | 1 | 177 | 8.81 |
| Dominique Zosso | 2 | 273 | 16.60 |