Computing Histogram of Oriented Gradients (HOG) as Local Shape Descriptor

This documentation explores the computation and application of Histogram of Oriented Gradients (HOG) as a local shape descriptor. HOG is an image processing technique that extracts features directly from input images through gradient magnitude and orientation calculations. The algorithm operates by first computing gradient vectors for each pixel using convolution kernels (typically Sobel or Prewitt filters), then accumulating gradient orientations into histogram bins within localized image cells. This approach captures shape characteristics and structural patterns in images, making it valuable for applications like object detection and facial recognition systems. By organizing gradient directions into histograms across different regions, we obtain discriminative features representing local shapes. The implementation involves key steps: gradient computation, spatial binning, block normalization, and feature vector concatenation. Using HOG enables deeper image analysis and extraction of meaningful visual information, thereby improving understanding and processing of image data for computer vision tasks.