Histogram of Oriented Gradients (HOG) is a feature descriptor used in computer vision and image processing for object detection by capturing the distribution of gradient orientations in localized portions of an image. It is particularly effective for detecting humans and other objects in images due to its ability to capture edge and gradient structures while being invariant to geometric and photometric transformations.

Histogram Of Oriented Gradients

Histogram of Oriented Gradients

A feature descriptor is a representation of an image or object that captures essential information about its features, enabling efficient matching and recognition tasks in computer vision. It transforms raw data into a structured format that facilitates comparison and analysis across different datasets or images.

feature descriptor

Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world. It combines techniques from image processing, machine learning, and neural networks to allow machines to recognize objects, track movements, and understand scenes in a manner similar to human vision.

computer vision

Image processing involves the manipulation and analysis of digital images to enhance their quality or extract valuable information. It is a crucial technology in fields like computer vision, medical imaging, and remote sensing, enabling advanced applications such as facial recognition, object detection, and image restoration.

image processing

Object detection is a computer vision task that involves identifying and locating objects within an image or video. It combines classification and localization to not only recognize what objects are present but also determine their positions in the visual data.

object detection

distribution of gradient orientations

localized portions of an image

detecting humans

Edge structures

gradient structures

Geometric transformations involve changing the position, size, and orientation of shapes in a coordinate plane while preserving certain properties. They are fundamental in fields like computer graphics, robotics, and physics, where manipulating spatial objects is crucial.

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