Image Processing with Features Extraction

This project focuses on advanced image processing techniques through feature extraction, enabling detailed analysis of visual data. Various feature extraction methods are applied, including statistical analysis, local binary patterns (LBP), k-means clustering, and image decomposition. This project provides a robust foundation for further applications in fields requiring high-resolution image analysis, such as healthcare, security, and autonomous systems.

Techniques and Methods

• Statistical Methods: Analyzes image distributions and pixel intensity values for basic classification.
• Local Binary Patterns (LBP): Uses LBP for texture classification, aiding in detecting edges, patterns, and surfaces.
• K-Means Clustering: Segments images into clusters based on similarity, allowing efficient object recognition and categorization.
• Image Decomposition: Breaks down images into component parts for detailed examination and analysis.