HOG Algorithm Implementation with MATLAB Code

This document provides MATLAB source code implementing the fundamental HOG (Histogram of Oriented Gradients) algorithm. As a widely adopted image feature descriptor, HOG is particularly effective for object detection and recognition tasks. The algorithm operates by dividing input images into small connected cells, computing gradient magnitudes and orientations for each pixel, and constructing orientation histograms within these cells. These histograms are then normalized across larger blocks to create illumination-invariant feature vectors. Key implementation aspects include: - Gradient computation using discrete differentiation filters ([-1,0,1] and [-1,0,1]') - 9-bin histogram generation for unsigned gradient orientations (0-180 degrees) - Block-based normalization using L2-norm or L2-Hys methods - Final feature vector concatenation from all blocks The provided MATLAB code demonstrates complete workflow from image preprocessing to feature extraction, enabling researchers to directly integrate HOG features into their computer vision pipelines. The modular structure allows straightforward customization of parameters like cell size (typically 8x8 pixels), block size (commonly 2x2 cells), and normalization schemes. This implementation serves as both an educational resource for understanding HOG mechanics and a practical foundation for developing object detection systems using classifiers like SVM. The code architecture separates core functionalities into distinct functions for gradient calculation, histogram generation, and block processing, facilitating easy modification and experimentation. Users can adapt the implementation for various applications by adjusting detection window sizes or incorporating different classification frameworks while maintaining the algorithm's core robustness to geometric and photometric transformations.