Wavelet Analysis of Soil Mass Images for Particle Size Determination

A method for determination of average grain size from images of fairly uniform particle size soil masses is presented. The procedure utilizes two-dimensional wavelet decomposition of gray scale images. Earlier attempts to quantify grain sizes based on the statistics of co-occurrence matrices suffered from dependence on the illumination intensity and soil color. By normalizing the energy distribution from wavelet decomposition the effects of these previously problematic factors have been eliminated. A general relationship between the center of area beneath the normalized energy distribution and the perceived particle size in pixels per diameter (PPD) is established. A sample problem demonstrates that the proposed wavelet decomposition method provides accurate grain sizes for a wide range of magnification levels as long as the resulting PPD is between approximately 1 and 50.

[1]  Roman D. Hryciw,et al.  Image Texture Analysis and Neural Networks for Characterization of Uniform Soils , 1998 .

[2]  J. David Frost,et al.  UNIFORMITY EVALUATION OF COHESIONLESS SPECIMENS USING DIGITAL IMAGE ANALYSIS , 1996 .

[3]  Bedrich J. Hosticka,et al.  A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms , 1996, Pattern Recognit..

[4]  Reed B. Freeman,et al.  Imaging Indices for Quantification of Shape, Angularity, and Surface Texture of Aggregates , 2000 .

[5]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[6]  Shobha K Bhatia,et al.  Application of Digital Image Processing in Morphological Analysis of Geotextiles , 1993 .

[7]  S. Beucher Use of watersheds in contour detection , 1979 .

[8]  Jun Zhang,et al.  Texture classification using neural networks and discrete wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

[9]  Shobha K Bhatia,et al.  Frequency Distribution of Void Ratio of Granular Materials Determined by an Image Analyzer , 1990 .

[10]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Pc Knodel,et al.  Quantification of Particle Shape and Angularity Using the Image Analyzer , 1991 .

[12]  J. D. Frost,et al.  A Revised Methodology to Minimize Bias in Determining the Porosity and Void Tensor of Particulate Media , 1993 .

[13]  J. Depinto,et al.  Image-based system for particle counting and sizing , 2000 .

[14]  John M Kemeny,et al.  Analysis of Rock Fragmentation Using Digital Image Processing , 1993 .

[15]  C. Kuo,et al.  Quantifying the fabric of granular materials an image analysis approach , 1994 .

[16]  Roman D. Hryciw,et al.  GRAIN-SIZE DISTRIBUTION OF GRANULAR SOILS BY COMPUTER VISION , 1997 .

[17]  Roman D. Hryciw,et al.  Soil Particle Size Distribution by Mosaic Imaging and Watershed Analysis , 1999 .

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  Markus H. Gross,et al.  Multiscale image texture analysis in wavelet spaces , 1994, Proceedings of 1st International Conference on Image Processing.

[20]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[21]  I. Daubechies Orthonormal bases of compactly supported wavelets , 1988 .

[22]  Daisheng Luo,et al.  AUTOMATIC ORIENTATION ANALYSIS OF PARTICLES OF SOIL MICROSTRUCTURES , 1992 .

[23]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[24]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.