Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Multifractal signature estimation for textured image segmentation
Pattern Recognition Letters
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
A new image segmentation algorithm with applications to image inpainting
Computational Statistics & Data Analysis
A conditional random field approach to unsupervised texture image segmentation
EURASIP Journal on Advances in Signal Processing
Local fractal and multifractal features for volumic texture characterization
Pattern Recognition
Exploiting intensity inhomogeneity to extract textured objects from natural scenes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
| Hi-index | 0.01 |
Multifractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based multifractal dimension estimations are most commonly used. However, in spite of its computational efficiency, the regular partition scheme used by various box-counting methods intrinsically produces less accurate results. In this paper, a novel multifractal estimation algorithm based on mathematical morphology is proposed and a set of new multifractal descriptors, namely the local morphological multifractal exponents is defined to characterize the local scaling properties of textures. A series of cubic structure elements and an iterative dilation scheme are utilized so that the computational complexity of the morphological operations can be tremendously reduced. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture mosaics and real images. The comparison results demonstrate that the morphological multifractal estimation can differentiate texture images more effectively and provide more robust segmentations.