Computer vision is a field of artificial intelligence that deals with teaching computers to interpret and understand digital images. Just like human vision, computer vision is concerned with all aspects of digital image processing, including acquisition, representation, analysis, and understanding.
Computer vision is a rapidly growing field with many applications in areas such as robotics, automotive, security, and manufacturing. In this post, we'll take a high-level overview of the field of computer vision and some of its key concepts.
At its core, computer vision is about teaching computers to understand digital images in the same way that humans do. This includes understanding the content of an image, such as objects, people, and scenery, as well as the context in which the image was taken.
Computer vision is a field of artificial intelligence that is closely related to machine learning. In general, computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from digital images.
The field of computer vision has its roots in the early days of artificial intelligence and machine learning. One of the earliest examples of computer vision is the work of David Marr on the recognition of human faces.
In the 1970s and 1980s, the field of computer vision began to take shape as a distinct field of research. This was driven in part by the development of new technologies, such as the charge-coupled device (CCD) sensor, which made it possible to acquire digital images.
The 1980s also saw the development of new algorithms for image processing and analysis, such as the Scale-Invariant Feature Transform (SIFT). These algorithms made it possible to detect and describe local features in digital images, which is a fundamental task in computer vision.
In the 1990s, the field of computer vision was further advanced by the development of new machine learning techniques, such as support vector machines (SVMs). These techniques made it possible to learn complex models from data, which is a key task in computer vision.
There are three key concepts in computer vision: image acquisition, image representation, and image analysis.
Image acquisition is the process of digital image acquisition, which is the conversion of an analog image into a digital image. This process is typically performed by an image sensor, such as a CCD sensor or a CMOS sensor.
The output of an image sensor is typically a two-dimensional array of digital values, which represents the intensity of the light at each pixel. This digital image can then be stored in a computer for further processing.
Image representation is the process of representing a digital image in a computer. This process typically involves converting the digital image into a format that can be stored in a computer, such as a JPEG file or a PNG file.
The image representation can also involve extracting features from the digital image, such as edges, corners, or blobs. These features can be represented as a set of points, which can be stored in a computer for further processing.
Image analysis is the process of extracting information from a digital image. This process typically involves extracting features from the image, such as edges, corners, or blobs, and then performing a statistical analysis of these features.
The output of image analysis is typically a set of quantitative measurements, which can be used to characterize the content of the image. For example, the output of image analysis can be used to identify objects in an image or to determine thepose of a person in an image.