Training computers to interpret and understand the visual world
using digital images and videos
What is Computer Vision?
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 (CV) 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.
Common Tools and Libraries
100E Use Cases
- A med-tech company uses computer vision to assess chronic wounds from images from smartphone camera
- Technologies used: TensorFlow/MobileNets
- A healthcare company uses computer vision to perform medical fraud detection
- Technologies Used: TensorFlow/MobileNets
- A semiconductor company uses computer vision to optimize the locations of circuit components on a semiconductor chip placement
- Technologies Used: TensorFlow/MobileNets, OpenCV
- Counting Road Traffic Capacity with OpenCV
- Face Recognition with Python (OpenCV)
- Link to article: https://realpython.com/face-recognition-with-python/
- Image Segmentation Using Color Spaces in OpenCV
- Link to article: https://realpython.com/python-opencv-color-spaces/