Seven Excellent Examples Of Computer Vision
Imagine all the things that human sight allows. And you can begin to realize the almost endless uses of computer vision. Here are some of the more compelling examples of computer vision in current practice:
Computer vision is needed to enable self-driving cars. Manufacturers such as Tesla, BMW, Volvo, and Audi use various cameras, lidar, radar, and ultrasonic sensors to map the environment. Their autonomous vehicles can recognize objects, ground markings, signals, and road signs—signage to drive safely.
Google Translate app
To read signs in a foreign language, you need to point the words at your camera phone and have the Google Translate app instantly tell you what it means in your preferred language. With optical character recognition to display the image and augmented reality to overlay an accurate translation, it is a handy tool that uses computer vision.
China is undoubtedly a leader in the use of facial recognition technology. It uses it for police functions, payment gateways, airport security checkpoints, and even distributing toilet paper and preventing paper theft in parks—Tiantan in Beijing, among many other uses.
Since 90 percent of all medical data is based on images, there are many possible uses for computer vision in medicine. And alsoy ou can expect our medical and professional facilities and our patients to benefit from activating new medical diagnostic methods, analyzing x-rays, mammograms. And other scans, and monitoring patients to identify problems earlier and help with operations. The future as implemented in health care.
Real-time sports monitoring
Ball and disc tracking has been standard in televised sports for some time. Still, Computer Vision also helps with game and strategy analysis, player performance and ranking, and player visibility tracking. Sponsorship of the brand in sports broadcasts.
At CES 2019, John Deere showcased a semi-autonomous combine harvester that uses artificial intelligence and computer vision to analyze grain quality during harvest and find the optimal path through crops. Computer vision also offers great potential in detecting weeds, so herbicides can be sprayed directly on them and not on the crops. It should reduce the required amount of herbicides by 90 percent.
Computer vision helps manufacturers work safer, smarter,and more efficiently in some ways.And also predictive maintenance is just one example of using machine vision to monitor equipment to take action before a failure causes costly downtime.Product packaging and quality are checked, and defective products are also reduced through machine vision.
Actual applications of computer vision are abundant, and the technology is still young. There fore humans and machines continue to work together, the workforce is freed to focus on higher value-added tasks as machines automate processes that rely on image recognition.
How Does Machine Vision Work?
Computer vision algorithms are generally based on convolutional neural networks or CNN. CNN’s typically use convolutional, cluster, ReLU. Fully connected, and lossy layers to simulate a visual cortex.
The convolution layer essentially takes up the integrals of many small, overlapping areas. The grouping layer performs some form of non-linear downsampling.
All though In a fully connected layer, neurons have networks to all activations of the previous layer. A loss layer calculates how network learning penalizes the gap between predicted and actual labels by using a softmax or cross-entropy loss for classification.
Machine Vision Training Datasets
Many public image datasets are helpful for visual training models.And also the simplest and oldest is the MNIST, which has 70,000 handwritten digits divided into ten classes, 60K for training and 10K for testing.
MNIST is an easy to model dataset even with a laptop without any acceleration hardware. CIFAR-10 and Fashion-MNIST are similar datasets of 10 classes. SVHN (Street View House Numbers) collects 600,000 images of real house numbers from Google Street View.
Image Processing Applications
Computer vision, while not perfect, is often good enough to be practical. A good example is the vision of self-driving cars.
Waymo, formerly Google’s self-driving car project, claims to have tested seven million kilometers of public roads and the ability to navigate daily traffic safely. There has been at least one accident involving a Waymo pickup truck. According to the police, the software should not be to blame.