Lane Detection Project using Color-based Clustering in MATLAB

In this project, we will use lane detection as a case study to explain in detail how to perform image clustering based on color information. Clustering is a fundamental image processing technique that groups pixels into collections with similar color or grayscale characteristics. This approach proves particularly valuable for lane detection as it helps identify road regions where vehicles travel. To achieve this objective, we will implement the project using MATLAB code. MATLAB is a widely-used programming language that provides numerous convenient tools and functions for digital image processing. Our implementation will utilize key MATLAB functions such as k-means clustering (through the kmeans function) for color segmentation, image preprocessing techniques for noise reduction, and region analysis methods for lane boundary identification. We will systematically explain each code section to ensure comprehensive understanding of the implementation process. The step-by-step breakdown will cover: color space conversion (from RGB to more perceptually uniform spaces like L*a*b*), feature extraction from pixel values, clustering parameter optimization, and post-processing techniques to refine lane detection results. This detailed approach enables users to fully grasp each computational stage and make necessary modifications or customizations according to specific requirements. The algorithm workflow involves: 1) Loading and preprocessing input images, 2) Converting color spaces for better clustering performance, 3) Applying clustering algorithms to segment road regions, 4) Using morphological operations to enhance lane features, and 5) Implementing Hough transforms or edge detection for final lane marking identification. Each step will be accompanied by corresponding MATLAB code snippets and explanations of underlying mathematical principles.