1. Object recognition: Machine learning algorithms can be trained to recognize and classify different objects in images or video. This can be used to improve image search engines, for example, or to enable autonomous vehicles to identify pedestrians and other objects.
2. Image classification: AI models can be used to classify images based on their content. For example, an image classification model could be used to identify whether an image contains a cat, a dog, or a car.
3. Object detection: Machine learning algorithms can be used to identify and locate objects within an image or video. This can be used to improve security systems, for example, by detecting people or vehicles in real-time.
4. Image segmentation: AI models can be used to segment images into different regions or categories, such as background, foreground, and different objects. This can be used to improve image editing software, for example, by separating the background from the foreground.
5. Image generation: Machine learning algorithms can be used to generate synthetic images that are difficult to distinguish from real ones. This can be used to improve image enhancement software, for example, by generating high-resolution versions of low-resolution images.
Overall, machine learning and AI models can be used to improve computer vision in a wide range of applications, enabling computers to better understand and interpret visual information.
