MATLAB Implementation of Integral Image for Efficient Haar Feature Computation

When computing Haar features, we can implement integral image functionality using MATLAB. An integral image is a data structure that enables efficient calculation of pixel sum within any rectangular region of an image. The implementation involves preprocessing the original image through cumulative row and column summations to generate a new representation where each pixel contains the sum of all pixels above and to the left. This allows computing any rectangular area sum using just four reference points through simple arithmetic operations: sum = D - B - C + A, where A, B, C, D represent corner coordinates. This method significantly optimizes Haar feature calculations by replacing O(n²) operations with O(1) constant-time lookups. Beyond Haar feature extraction, this technique finds applications in various computer vision tasks including object detection algorithms like Viola-Jones, image segmentation, and rapid filtering operations. The MATLAB implementation typically utilizes cumsum() functions for efficient integral image generation and vectorized operations for optimal performance.