Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers & Geosciences - Special issue on three-dimensional reconstruction, modelling and visualization of geologic materials
Towards 3-D petrography: application of microfocus computer tomography in geological science
Computers & Geosciences - Geological Applications of Digital Imaging
The Structure of Locally Orderless Images
International Journal of Computer Vision
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image registration by local histogram matching
Pattern Recognition
Using Basic Image Features for Texture Classification
International Journal of Computer Vision
PhaseQuant: A tool for quantifying tomographic data sets of geological specimens
Computers & Geosciences
Form From Projected Shadow (FFPS): An algorithm for 3D shape analysis of sedimentary particles
Computers & Geosciences
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In micro-CT images of meteorites individual components such as matrix, chondrules, Ca,Al-rich inclusions (CAIs), and opaque phases (metal and sulfide) are visually distinguishable. Automated classification of the components is desirable to deal with the large amount of data in a 3-D CT image. Classification by pixel intensity achieves a performance only 25% of the way from baseline to perfect. The poor performance is explained by an overlap in the range of intensities present in the different components. An improved method of semiautomated classification is presented, based on local histograms of the intensity. This achieves a performance 60% of the way from baseline to perfect.