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Evolved Epithelial Modulation Project

https://github.com/Jrbiltmore/UnCamo

This project aims to detect evolved epithelial modulation using IR and visible spectrum data, combining advanced image processing techniques with machine learning algorithms.

Project Overview

The detection methods include:

• Spectral signature analysis to examine pixel intensity changes across different wavelengths.
• Polarization detection to identify anomalies in light reflectance using polarized filters.
• Anomaly detection through absolute difference, structural similarity index (SSIM), and thresholding methods.
• Machine learning models such as Convolutional Neural Networks (CNNs) for classification and detection tasks.

Image Preprocessing

Before the modulation detection, the images undergo several preprocessing steps to standardize and normalize their format:

• Normalization: Image intensities are normalized to a common range to ensure uniform contrast and brightness.
• Resizing: All images are resized to a standard dimension to facilitate machine learning training and processing.
• Gaussian Blur: A Gaussian blur is applied to reduce noise and enhance feature extraction.

Spectral Signature Analysis

Spectral signature analysis examines the intensity of pixel values across various light wavelengths. The following equation shows how the histogram for each color channel is computed:

$$\text{Histogram}(I) = \sum_{x,y} \delta (I(x,y) - i)$$

Where $I(x,y)$ is the intensity of a pixel at position $(x,y)$, and $i$ is a specific intensity level. The peaks in the histogram identify areas of interest.

Polarization Detection

In polarization analysis, a rotation of polarized light is used to calculate the degree of polarization (DoP). The degree of polarization is given by:

$$\text{DoP} = \frac{\sqrt{Q^2 + U^2}}{I}$$

Where $Q$ and $U$ are the Stokes parameters, and $I$ is the intensity of the unpolarized light.

Anomaly Detection

Anomalies are detected by comparing images captured from different spectra (e.g., IR and visible light). The Structural Similarity Index (SSIM) measures the similarity between two images:

$$\text{SSIM}(x, y) = \frac{(2 \mu_x \mu_y + c_1)(2 \sigma_{xy} + c_2)}{(\mu_x^2 + \mu_y^2 + c_1)(\sigma_x^2 + \sigma_y^2 + c_2)}$$

Where $\mu_x$, $\mu_y$ are the means of $x$ and $y$, $\sigma_x^2$, $\sigma_y^2$ are the variances, and $\sigma_{xy}$ is the covariance of $x$ and $y$. This helps to detect regions with significant structural differences.

Machine Learning Models

We use CNNs to classify modulated versus non-modulated areas in the images. A CNN model is built using multiple layers of convolutional filters followed by pooling layers. The final fully connected layers classify the detected features.

The training process uses a categorical cross-entropy loss function:

$$L = -\sum_{i=1}^{N} y_i \log(p_i)$$

Where $y_i$ is the true label and $p_i$ is the predicted probability for class $i$.

Conclusion

This project combines advanced imaging and machine learning techniques to detect evolved epithelial modulation in biological tissues, providing a comprehensive framework for analysis across IR and visible spectra.