El Histograma de Gradientes Orientados (HOG) Descriptor is a powerful de extracción de características utilizado en visión por computadora and image processing, primarily for object detection tasks. Developed by Dalal and Triggs in 2005, HOG captures the structure or shape of an object within an image by analyzing the distribution of intensity gradients or edge directions.
The HOG Descriptor works by dividing an image into small connected regions called cells. For each cell, the gradient (or change in pixel intensity) is calculated, and a histogram of gradient orientations is created. These histograms are then normalized across larger blocks of cells to improve the descriptor’s robustness frente a cambios en la iluminación y el contraste.
This normalization step helps to create a more stable feature set, making it easier for aprendizaje automático algorithms to classify objects accurately. The final HOG Descriptor is a concatenation of these histograms, producing a high-dimensional feature vector that represents the visual characteristics of the object in the image.
HOG Descriptors are especially effective for detecting objects like pedestrians in images and have been widely adopted in various applications, including surveillance systems, vehículos autónomos, and robotics. The method’s strength lies in its ability to capture local shape information while being invariant to changes in lighting and small deformations.
En general, el Descriptor HOG es una herramienta fundamental en el campo de la visión por computadora, permitiendo que las máquinas reconozcan e interpreten datos visuales de manera efectiva.