What Does Computer Vision Mean?
Computer vision (CV) is the subcategory of artificial intelligence (AI) that focuses on building and using digital systems to process, analyze and interpret visual data. The goal of computer vision is to enable computing devices to correctly identify an object or person in a digital image and take appropriate action.
Uses for computer vision include:
- Biometric access management — CV plays an important role in both facial and iris recognition.
- Industrial robots and self-driving cars — CV allows robots and autonomous vehicles to avoid collisions and navigate safely.
- Digital diagnostics — CV can be used in tandem with other types of artificial intelligence programming to automate the analysis of X-rays and MRIs.
- Augmented reality — CV allows mixed reality programming to know where a virtual object should be placed.
Techopedia Explains Computer Vision
Computer vision includes image processing as well as pattern recognition and object detection. It has a wide range of practical applications, from self-driving cars and facial recognition technology to medical imaging and augmented reality.
Computer Vision and Self-Driving Cars
Here are some ways that computer vision is used in self-driving cars:
- Allow self-driving cars to identify and distinguish objects such as traffic lights, pedestrians and road signs.
- Detect and track the edges of the road.
- Plan a path through traffic by predicting the motion of other objects in the environment.
Computer Vision and Facial Recognition
Here are some ways that computer vision is used in facial recognition:
- Identify specific facial features and use this information to determine whether a face is present.
- Extract a set of unique facial features such as the distance between the eyes or the shape of the nose to create a template that represents a specific individual’s face.
- Match the template with a single image in the database.
Computer Vision and Medical Imaging
Here are some ways that computer vision is used in medical imaging:
- Segment medical images into different regions of interest, such as organs or tissues.
- Remove noise and enhance image contrast to make it easier for doctors to identify abnormalities.
- Support computer-aided diagnosis systems that analyze medical images and provide physicians with diagnostic support.