In our previous post, we discussed about Computer Vision with an example.
In this post, lets have a look at some fields where Computer Vision is being used. Computer vision is one of the areas in Machine Learning where core concepts are already being integrated into major products that we use every day.
CV In Self-Driving Cars
Computer vision enables self-driving cars to make sense of their surroundings. Cameras capture video from different angles around the car and feed it to computer vision software, which then processes the images in real-time to find the extremities of roads, read traffic signs, detect other cars, objects and pedestrians. The self-driving car can then steer its way on streets and highways, avoid hitting obstacles, and (hopefully) safely drive its passengers to their destination.
CV In Facial Recognition
Computer vision also plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities. Computer vision algorithms detect facial features in images and compare them with databases of face profiles. Consumer devices use facial recognition to authenticate the identities of their owners. Social media apps use facial recognition to detect and tag users. Law enforcement agencies also rely on facial recognition technology to identify criminals in video feeds.
CV In Augmented Reality & Mixed Reality
Computer vision also plays an important role in augmented and mixed reality, the technology that enables computing devices such as smartphones, tablets and smart glasses to overlay and embed virtual objects on real world imagery. Using computer vision, AR gear detect objects in real world in order to determine the locations on a device’s display to place a virtual object. For instance, computer vision algorithms can help AR applications detect planes such as tabletops, walls and floors, a very important part of establishing depth and dimensions and placing virtual objects in physical world.
CV In Healthcare
Computer vision has also been an important part of advances in health-tech. Computer vision algorithms can help automate tasks:-
Cancer Detection
COVID-19 Diagnosis
Cell Classification
Movement Analysis
Tumor Detection
CV In Agriculture
The agricultural industry has witnessed several contributions of computer vision-artificial intelligence (AI) models in areas such as planting, harvesting, advanced analysis of weather conditions, weeding and plant health detection and monitoring. Some of the most noteworthy contributions that exist today are:
Crop Monitoring
Flower Detection
Plantation Monitoring
Insect Detection
Plant Disease Detection
CV In Transportation
Computer vision is playing a key role in this transportation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges including imaging under a variety of environmental and illumination conditions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns.
Conclusion
Despite the recent progress, which has been impressive, we’re still not even close to solving computer vision. However, there are already multiple healthcare institutions and enterprises that have found ways to apply CV systems, powered by CNNs, to real-world problems. And this trend is not likely to stop anytime soon. If you have any questions, feel free to leave it in the comment section. Want to get in touch?, You can connect with me on Instagram and LinkedIN Thanks for reading!😄 🙌
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