Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical synthesis of accurate fractal images
Graphical Models and Image Processing
A practical method for estimating fractal dimension
Pattern Recognition Letters
The gliding box method for multifractal modeling
Computers & Geosciences - Fractals and Multifractals
Near optimum estimation of local fractal dimension for image segmentation
Pattern Recognition Letters
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of fractal signals from noisy measurements usingwavelets
IEEE Transactions on Signal Processing
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
A review of the fractal image coding literature
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation
IEEE Transactions on Image Processing
Material detection based on fractal approach
Proceedings of the 9th International Conference on Advances in Mobile Computing and Multimedia
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Fractal theory provides a powerful mathematical tool for texture segmentation. However, in spite of their increasing popularity, traditional fractal features are intrinsically of less accuracy due to the difference between the idea fractal model and the fractal reality of digital images. In this paper, we incorporated the multifractal analysis method into the idea of fractal signature, and thus proposed a novel type of texture descriptor called multifractal signature, which characterizes the variation of multifractal dimensions over spatial scales. In our approach, the local multifractal dimension of each scale was calculated by using the measurement acquired at two successive scales so that the time-consuming and less accurate least square fit was avoided. Based on three popular multifractal measurements, the differential box-counting (DBC) based multifractal signature, relative DBC based multifractal signature, and morphological multifractal signature were presented in this paper. The performance of the proposed texture descriptors was evaluated for segmentation of texture mosaics by comparing to the corresponding multifractal dimensions. The experimental results demonstrated that multifractal signatures can differentiate textured images more effectively and provide more robust segmentations.