Texture description and segmentation through fractal geometry
Computer Vision, Graphics, and Image Processing
Handbook of pattern recognition & computer vision
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
A framework for texture analysis based on spatial filtering
A framework for texture analysis based on spatial filtering
Medical Imaging and Osteoporosis: Fractal's Lacunarity Analysis of Trabecular Bone in MR Images
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Fractional Box-Counting Approach to Fractal Dimension Estimation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
An Improved Differential Box-Counting Approach to Compute Fractal Dimension of Gray-Level Image
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 01
An improved box-counting method for image fractal dimension estimation
Pattern Recognition
Lacunarity analysis of raster datasets and 1D, 2D, and 3D point patterns
Computers & Geosciences
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
| Hi-index | 0.08 |
Fractal dimension measures the geometrical complexity of images. Lacunarity being a measure of spatial heterogeneity can be used to differentiate between images that have similar fractal dimensions but different appearances. This paper presents a method to combine fractal dimension (FD) and lacunarity for better texture recognition. For the estimation of the fractal dimension an improved algorithm is presented. This algorithm uses new box-counting measure based on the statistical distribution of the gray levels of the ''boxes''. Also for the lacunarity estimation, new and faster gliding-box method is proposed, which utilizes summed area tables and Levenberg-Marquardt method. Methods are tested using Brodatz texture database (complete set), a subset of the Oulu rotation invariant texture database (Brodatz subset), and UIUC texture database (partial). Results from the tests showed that combining fractal dimension and lacunarity can improve recognition of textures.