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

• Type: Conference
• Year: 2005
• Where: VLSM
• Authors: Georgios Evangelopoulos, Iasonas Kokkinos, Petros Maragos
• Keywords: Complex Textured Images, Computer Vision, Textured Images, Variational Image Segmentation, VLSM 2005