Horizontal and Vertical Gray-Level Projection

Gray-level projection is a fundamental technique in image processing, primarily used to analyze the distribution characteristics of pixel intensities along horizontal or vertical directions. Horizontal projection calculates the sum of gray values for each row of pixels, while vertical projection computes the sum for each column. These methods are commonly applied in scenarios such as text segmentation and object localization.

The core implementation approach in MATLAB involves: First converting the image to a grayscale matrix, then using summation functions to accumulate values row-wise and column-wise. Horizontal projection generates a 1D array through row-wise summation (e.g., using sum(I,2) where I is the grayscale image), where peaks typically correspond to vertical positions of text lines or objects. Vertical projection is achieved through column-wise summation (sum(I,1)), with peaks indicating horizontal distribution of targets.

The advantages of this method include computational simplicity and notable effectiveness for binary images, making it suitable for processing scanned documents or high-contrast images. In practical applications, accuracy can be further enhanced by combining threshold processing or peak detection algorithms (e.g., findpeaks function) to optimize projection results.