Computer vision is one of the astonishing things that have happened from artificial intelligence and deep learning in this digital world. The technology advancement and the introduction of AI and deep learning techniques in computer vision systems have made it capable of replicating human vision. Computer vision (CV) has been more commercialized in the last few years. It is deployed in a varied range of applications. Computer Vision is increasing in demand and used in different areas such as biometric analysis in security, quality inspection in manufacturing, guiding autonomous vehicles in transportation, and many more.
Computer vision is one of the advance technology which uses artificial intelligence. With the help of deep learning and AI, computers are trained in order to interpret the visual world. The camera capture digital images and video and by the use of deep learning machines identify and classify the images accurately. The images are interpreted through pattern or object recognition.
The fundamentals of computer vision were put in the 1950s. During its early stage, neural networks were used to detect the edges of any object. It then became able to sort objects into basic categories such as square and circles. For the first time, in the 1970s, computer vision was commercially used to interpret a handwritten text by the use of optical character recognition.
With the penetration of the internet, in the late 1990s, it became possible to analyze large sets of images online by using computer vision. Then came the facial recognition system. And from the past two decades, it showed huge advancement, it can now detect visual inputs more accurately and quickly than humans.
Images or videos are captures using cameras in real-time. Thousands of the captured images are then compiled. Once a sufficient amount of data is gathered, it is then processed using deep learning models. For this, the software breaks the images into a grid of boxes and assign a number to each grid. These numeric values are then interpreted by the computer.
After understanding the images, they classified on the basis of shape and sizes. Nowadays there are many types of computer system available which are deployed for a specific purpose such as image segmentation, object detection, pattern detection, edge detection, feature matching, etc.
The computer vision’s hardware components consist of a camera, co-processors, lighting, optical components, frame grabbers, etc. new and some more advanced hardware has been launched that makes the installation process easier, improved the resolution, and fully digital handling. They also facilitate a variety of networking architecture that offers scope for diverse applications.
Software is a very important component of the computer vision system. The major function that is performed by the software includes image classification, object detection, object tracking, and content-based retrieval of images. Tech giants are focused to assist companies in developing more advanced software.
Smart camera-based computer vision is of compact dimension, simple, and integrated with a smart camera. A Smart Camera is a self-contained unit. It contains more flexible tools, inherently it is capable of being programmed in order to handle various types of imaging algorithms and application functions.
The Pc-based computer vision is mainly devoted to image processing. This requires many other peripheral devices to perform tasks like data transfer, lighting storage, and frame grabbing. If the PCs are multicomponent configured then it will result in high fragility.
There is a variety of applications of computer vision and it is used for various purposes such as Quality assurance and inspection, measurement, identification, 3D visualization and modeling, positioning guidance. Computer vision, along with deep learning algorithms has enabled the inspection automation of almost every product in the line manufacturing. It is also beneficial in streamline code labeling and quality control.
The computer vision market has been witnessing rapid growth due to the advancement of technology and exploitation of AI and deep learning in computer vision has made it more accurate and efficient. Computer vision technology is predominantly being adopted worldwide. Applications of CV includes machine security and surveillance, vision and gaming, and others. Due to its accuracy and efficiency in performance, computer vision adoption will be increased in the coming years.
Apart from the application in smartphones and mobiles, it is also adopted in other devices like drones, augmented reality, and driverless cars. It is expected that the more technological innovation will improve the hardware and software component of the computer vision which will to improved resolution, greater sensitivity, and faster interfaces. The Global computer vision Market has been anticipated to rise at a growth rate of 10.1% CAGR over the forecast period.
