Current Work
Porosity-Mini
The Porosity-Mini algorithm applies linear regression methods to analyze high-resolution microscopic images of materials at the micron scale. This method quantifies material porosity by examining the underlying distribution patterns of pixel intensity, providing an accurate percentage porosity as the output. The approach is particularly effective for materials with consistent and well-defined structures.
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Porosity represents an enhanced approach to material analysis by utilizing advanced deep learning techniques. At its core, this method employs a regressive neural network trained on a dataset of approximately 10,000 microscopic images. By leveraging the network's capability to detect intricate distributions, Porosity achieves more precise quantification of material porosity from image data transformed to pixel intensity distribution.
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