An Introduction to Computer Vision
If we look at the very idea of what Computer Vision can achieve, it would seem almost unrealistic to the unversed. That is how fascinating Computer Vision is. It is very closely related to how human vision works, though human vision still remains the more sophisticated of the two. If we look around us, we see and identify multiple things and our reactions to our environment depend on a lot of factors and is a complex process, involving not only what we actually see, but also our understanding and perception formed through millions of interactions we have had with our environment. Computer vision seeks to replicate this process of analyzing the visual medium and reacting accordingly to it.
Talking about the technical aspect of Computer vision and how it works, it is a field of artificial intelligence that focuses on enabling computers to interpret and understand the visual world. It involves the development of algorithms and systems that can analyze and understand images and videos, and extract useful information from them.
How Computer Vision Is Impacting Industries Worldwide?
If we analyze the core fundamentals of the Fourth Industrial Revolution or Industry 4.0, Computer vision is one of the key technologies that has defined this wave of industrial revolution. The incorporation of these path-breaking technologies has changed the way companies make, improve and sell their products. Industries are integrating new technologies such as the Internet of Things (IoT), cloud computing and analytics, and AI and computer vision across their manufacturing facilities and operations.
Computer Vision is being used to optimize production processes, improve quality control and reduce costs in manufacturing. Analyzing customer behavior to improve the shopping experience, making road safety easier, personalizing student experience, making traffic management convenient, providing an immersive experience in entertainment, security etc - Computer vision has presence practically everywhere!
Role of AI-based Computer Vision in Healthcare
Computer Vision has vast uses in the Healthcare industry in terms of how it can improve the accuracy of medical treatments and procedures. It also helps with the decision making process, in choosing the best possible treatment course for a patient.
Digital Imaging/ Image Analysis: Computer Vision is rapidly becoming an alternative solution to the problem of shortage of Radiologists and MRI technicians in Healthcare. With a high accuracy rate, improved speed and reduced margin of errors, Computer Vision can recognize patterns and make diagnosis in medical images much easier.
Detection of Tumors/Cancers : Artificial intelligence (AI) is playing an important role in tumor and cancer detection. One way AI is being used for this purpose is by analyzing medical images such as X-rays, CT scans and MRIs. Using machine learning algorithms, AI systems can learn to recognize patterns and features associated with different types of tumors and cancers and can be used to identify these abnormalities in new images.
AI can also be used to analyze electronic medical records and other data sources to identify patterns and trends that may indicate the presence of a tumor or cancer. For example, AI systems can be trained to analyze a patient's medical history, test results, and demographic information, identify risk factors for certain types of tumors and cancers, and recommend screening and preventive measures.
Surgical Guidance :
Surgical Guidance: Machine vision is being used to provide the surgeon with real-time images of the surgical site, highlighting critical structures and guiding the surgeon's instruments.
Surgical Assist: Computer vision can be used to help surgeons perform complex tasks such as suturing and tissue manipulation.
Quality Control: Computer vision can be used to monitor surgical quality by checking for bleeding or tissue damage.
Training: Machine vision can be used to train surgeons by providing them with a simulated surgical environment, allowing them to practice the procedure before performing surgery on a real patient.
Role of AI-based Computer Vision Is Making Our Roads More Safe and Secure
Leveraging how Computer Vision can help computers interpret and understand visual data from the world around them, this technology is the answer to a lot of safety challenges with regards to road safety. Computer vision is making cars safer in numerous ways, by enabling them to perceive and understand their surroundings.
Driver Behaviour Analysis : Computer vision analysis of driver behavior involves the use of computer vision algorithms and techniques to analyze driver video footage or live video feeds to understand and evaluate driver behavior while behind the wheel. It can be used to improve driver safety, detect dangerous behavior and optimize vehicle performance.
Gesture and facial expression analysis: It uses computer vision algorithms to analyze the driver's gestures and facial expressions to understand the driver's mood and attentiveness. For example, a tired or distracted driver may exhibit different facial expressions and body language than an attentive and focused driver.
Lane Detection and Tracking: This uses computer vision algorithms to detect and track lanes on the road and assess whether the driver is keeping or deviating from the lane.
Vehicle Tracking: This uses computer vision algorithms to track the movement of vehicles within the camera's field of view and assess their speed, acceleration and deceleration. This can be used to detect dangerous driving behavior such as speeding or sudden lane changes.
Self Driving/Autonomous Cars : One of the best-known applications of computer vision in the automotive field is Autonomous/Self-driving cars. From self-driving non-commercial vehicles to large logistics trucks, computer vision and AI-enabled self-driving systems are revolutionizing the automotive industry. These cars are equipped with high-definition cameras and computer vision systems work in the background to recognize roads, objects, people and other vehicles to control the movement of the vehicle. A combination of sensors and machine learning algorithms is used to navigate and understand the surroundings, and can be programmed to obey traffic rules and avoid collisions.
Traffic Road Sign Detection : Computer vision traffic sign recognition uses computer vision algorithms and techniques to automatically identify and classify traffic signs in images or video footage. This not only improves navigation and safety in autonomous vehicles, but can also be used for many other applications such as traffic management and traffic safety analytics.
With the massive scalability that we see in the use of such technologies, Computer Vision will be used to perform a broader range of functions in the near future. Massive adoption of CV can be expected with the kind of buzz that it is creating across industries. This would just be the right time to start investing in it.