COMPUTER VISION

Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images and videos.

Computer vision uses artificial intelligence, specifically machine learning and neural networks to teach computers to derive meaningful information from digital images, videos and other visual inputs.

Computer vision train machines to see using cameras, sensors, data and algorithms in place of retina, optic nerves and a visual cortex.

There are various types of classifications of computer vision and they are; image classification, object detection, image segmentation, facial recognition and motion analysis.

Image classification is the process of assigning a label or category to an image based on its content. Image classification algorithms use machine learning techniques to teach and extract features from images and classify them into predefined categories.

Object detection is the process of locating and identifying objects within an image or video. Object detection algorithms use machine learning techniques to detect object and draw bounding boxes around them.

Image segmentation is the process of dividing an image into its constituent parts or regions. Image segmentation algorithms use machine learning techniques to identify and separate objects or regions within an image.

Facial recognition is the process of identifying and verifying individuals based on their facial features.

Facial recognition algorithms use machine learning techniques to extract features from facial images and match them to known identities.

Motion analysis is the process of analyzing the movement of objects or individuals within a video or a sequence of images. Motion analysis algorithms use machine learning techniques or tracking algorithms to detect and analyze motion within a scene.

Computer vision systems consist of hardware and software components that work together to process and analyze visual data.

The hardware components of a computer vision system are as follows; cameras, processor, memory and sensors.

The cameras are the primary hardware component of a computer vision system. It is used to capture the images or videos of the environment.

The processors used in computer vision systems are employed to process and analyze the visual data captured by the camera. The processors are usually any or a mix or all of the main types such as CPU, GPU or NPU.

The memory such as RAM or Storage is used to store the visual data and processing results.

The sensors such as depth sensors or LIDAR can be used to capture additional data such as 3D information.

The software components of a computer vision system are as follows; image processing algorithms, object detection and recognition algorithms, machine learning models and computer vision libraries.

The image processing algorithms are used to enhance, filter or transform the visual data.

The object detection and recognition algorithms are used to identify and classify objects within the visual data.

The computer vision libraries such as openCV provide pre-built functions and tools for computer vision tasks.

The advantages of computer vision are as follows; computer vision can improve accuracy and precision in its various applications. Computer vision can automate repetitive and time consuming tasks. Computer vision can improve safety in various surveillance and security applications. Computer vision can reduce labor cost and improve productivity.

The disadvantages computer vision is as follows; computer vision can require high quality data to function effectively and poor quality data can lead to biased or inaccurate results. Computer vision may not always understand the context or nuances of visual information leading to errors or misinterpretations. Computer vision can be vulnerable to security risks such as data breeches or adversarial attacks. Computer vision may be biased towards certain groups or individuals leading to unfair or discriminatory outcomes.

The application of computer vision is widespread and covers most industries such as health care, security and surveillance, manufacturing, transportation and retail where they are used to monitor and record, analyze and calibrate events. Computer vision systems in their operation are also used to detect anomaly in vital images and correct errors in real time.

The future of computer vision depends on the trends and developments in the following technologies. Advances in deep learning will improve its accuracy and enhances the qualities of it images thereby triggering and accelerating the use of computer vision systems in all its forms. The increased adoption of computer vision system whether as an headgear or a pair of glasses will enhance the ability of computer vision systems to be used to monitor homes, business and manufacturing operations not only effectively but also remotely. Computer vision is expected to integrate with artificial intelligence and internet of things systems thereby enabling real time processing and analysis of visual data. The future will also bring into focus the ways to resolve pending ethical issues in terms of eliminating bias and unfairness and transparency associated with their use.

 

SOURCES:

• Computer vision: Algorithms and applications by Richard Szeliski.
• Computer vision: A modern approach by David A. Forsyth and Jean Ponce.
• Computer vision: Models, learning and interference by Simon J.D Prince.
• Deep learning for vision systems by Mohamed Elgendy.
• Concise computer vision: An introduction into theory and algorithms by Reinhard Klette.