Computer vision is a field of computer science that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide appropriate output. It is like imparting human intelligence and instincts to a computer. In reality, it is a difficult task to enable computers to recognize images of different objects.
Computer vision is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly.
Computer vision’s goal is not only to see, but also process and provide useful results based on the observation. For example, a computer could create a 3D image from a 2D image, such as those in cars, and provide important data to the car and/or driver. For example, cars could be fitted with computer vision which would be able to identify and distinguish objects on and around the road such as traffic lights, pedestrians, traffic signs and so on, and act accordingly. The intelligent device could provide inputs to the driver or even make the car stop if there is a sudden obstacle on the road.
When a human who is driving a car sees someone suddenly move into the path of the car, the driver must react instantly. In a split second, human vision has completed a complex task, that of identifying the object, processing data and deciding what to do. Computer vision’s aim is to enable computers to perform the same kind of tasks as humans with the same efficiency.
Computer Vision and Image Processing
Computer vision is distinct from image processing.
Image processing is the process of creating a new image from an existing image, typically simplifying or enhancing the content in some way. It is a type of digital signal processing and is not concerned with understanding the content of an image.
A given computer vision system may require image processing to be applied to raw input, e.g. pre-processing images
Examples of image processing include:
- Normalizing photometric properties of the image, such as brightness or color.
- Cropping the bounds of the image, such as centering an object in a photograph.
- Removing digital noise from an image, such as digital artifacts from low light levels.
Computer vision can be used to detect anomalies automatically. For instance, a company can use computer vision if a product is made as it was intended to be made. This can be useful for large scale and precision needing operations.
Self-driving cars is one of the biggest use case of computer vision. A car to navigate by itself, needs a massive amount of visual feed and processing the data in real time.
The agriculture industry is also employing computer vision technology to make operations more efficient such as growing methods, yielding more crops and generating higher profits. An interesting use case is a company called SlantRange, which uses drones with computer vision cameras to scan the crops and determine whether they are under threat or not. The drone hovers at an altitude of about 400 feet with a 4.8 cm/pixel resolution camera. Once it is airborne, the camera takes pictures of the crops which help identify possible hazardous conditions such as infestation, lack of water nutrition. It also makes estimates what the crop yields will be when it is time to harvest. All this data is funneled into an analytical system which provides data insights and allows farmers to take action in order to save their crops.
Computer vision is used to identify and diagnose conditions and illnesses and make lifesaving medical interventions. There have been some arguments in healthcare which one is better: computer vision vs sensors for smart healthcare. There is really no need to pin one against the other because computer vision must be used along with the sensors to producing better results. sensors detect the amount of blood located on surgical sponges which are then processed by machine learning algorithms which make a determination how much blood was lost. The technology is currently being used in surfing surgical operations and Caesarian deliveries.
Examples of fluorine 18 fluorodeoxyglucose PET images
- Computer vision identifies signs of early Alzheimer’s up to 6 years before clinical diagnosis
Quality control is one of the main applications of AI. Quality control is very important part of manufacturing. In most places, quality control takes as much time as manufacturing. Sometimes it becomes impossible to guarantee it. AI plays a main role these times. AI based quality control can automate the process to minimize error and time take to do it.