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2005
Springer

Advances in Variational Image Segmentation Using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration

10 years 8 months ago
Advances in Variational Image Segmentation Using AM-FM Models: Regularized Demodulation and Probabilistic Cue Integration
Current state-of-the-art methods in variational image segmentation using level set methods are able to robustly segment complex textured images in an unsupervised manner. In recent work, [18, 19] we have explored the potential of AM-FM features for driving the unsupervised segmentation of a wide variety of textured images. Our first contribution in this work is at the feature extraction level, where we introduce a regularized approach to the demodulation of the AM-FM -modelled signals. By replacing the cascade of multiband filtering and subsequent differentiation with analytically derived equivalent filtering operations, increased noise-robustness can be achieved, while discretization problems in the implementation of the demodulation algorithm are alleviated. Our second contribution is based on a generative model we have recently proposed [18, 20] that offers a measure related to the local
Georgios Evangelopoulos, Iasonas Kokkinos, Petros
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where VLSM
Authors Georgios Evangelopoulos, Iasonas Kokkinos, Petros Maragos
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