Object recognition is a subfield of computer vision, often referred to as object-based image understanding. It is the task of a computer to recognize objects within an image or video. It is also sometimes referred to as automatic recognition or object detection.
Object recognition, and its related tasks such as object classification, have assisted with automation, such as in driverless cars and robotics. In such scenarios, the computer needs to analyze an image and determine what objects are present, what type of the object is present, and where it is relative to other objects within the image.
Object recognition is composed of several stages. At the first step, region of interest detection is performed. algorithms are employed to detect important candidates in an image, which can range in size and shape, such as circles, lines, or straight edges. Secondly, feature extraction is done, by computing a numerical description of each detected object, such as its average color or edges.
Once these features have been extracted, it is time to compare them with previously known objects. This comparison is done using various algorithms that can find the best match. Once a match has been found, the identified object is often referred to as the “target.”
Object recognition is used for various applications. It is used to identify faces in images, for computer security and authentication. It is also used for industrial automation and robotics, medical imaging, motion tracking, and computer animation.
In recent years, object recognition has been improved through deep learning, a powerful form of machine learning. Through deep learning, the computer can rapidly analyze large amounts of data and use it to identify objects. Object recognition techniques have become more accurate and more reliable than ever before.
