Abstract:
Most multiresolution methods of segmenting images have hitherto resolved an image into successively coarser scales and discarded high-pass edge information. We present a ...Show MoreMetadata
Abstract:
Most multiresolution methods of segmenting images have hitherto resolved an image into successively coarser scales and discarded high-pass edge information. We present a robust and computationally tractable method for the automated segmentation of images using spatially varying kernels derived from multiscale edge information of the image. We include examples of segmentation of synthetic and real images that demonstrate the performance of the algorithm in preserving fine detail and edge information in the segmentation maps while being robust to heavy noise.
Date of Conference: 22-25 September 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7803-7622-6
Print ISSN: 1522-4880
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Segmentation ,
- Synthetic Images ,
- Edge Information ,
- Segmental Information ,
- Maximum Likelihood Estimation ,
- Fine Structure ,
- Image Pixels ,
- Expectation Maximization ,
- Hidden Markov Model ,
- Class Membership ,
- Step Function ,
- Field Images ,
- Wavelet Transform ,
- Gaussian Mixture Model ,
- Filtration Performance ,
- Homogeneous Regions ,
- Neighboring Pixels ,
- Bias Term ,
- Statistical Dependence ,
- Neighborhood Structure ,
- Probability Of Pixel ,
- High Frequency Information ,
- Gaussian Density Function
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Image Segmentation ,
- Synthetic Images ,
- Edge Information ,
- Segmental Information ,
- Maximum Likelihood Estimation ,
- Fine Structure ,
- Image Pixels ,
- Expectation Maximization ,
- Hidden Markov Model ,
- Class Membership ,
- Step Function ,
- Field Images ,
- Wavelet Transform ,
- Gaussian Mixture Model ,
- Filtration Performance ,
- Homogeneous Regions ,
- Neighboring Pixels ,
- Bias Term ,
- Statistical Dependence ,
- Neighborhood Structure ,
- Probability Of Pixel ,
- High Frequency Information ,
- Gaussian Density Function