Machine learning (ML) has revolutionized computer vision, enabling machines to interpret and understand visual data. Here are key applications:

1.Image Classification: ML models, particularly convolutional neural networks (CNNs), can classify images into predefined categories, such as identifying objects in photos.

2.Object Detection: Beyond classification, ML algorithms can detect and locate objects within an image, providing bounding boxes around detected items. This is crucial for applications like autonomous driving and surveillance.

3.Image Segmentation: This involves partitioning an image into meaningful regions. Semantic segmentation labels each pixel with a class, while instance segmentation distinguishes between different objects of the same class.

4. Facial Recognition: ML algorithms can identify and verify individuals from images or video frames. This technology is widely used in security, social media tagging, and personalized user experiences.

5. Optical Character Recognition (OCR): ML models convert different types of documents, like scanned paper documents or PDFs, into editable and searchable data.

6. Medical Imaging: ML aids in diagnosing diseases by analyzing medical images such as X-rays, MRIs, and CT scans, helping in early detection and treatment planning.

7. Augmented Reality (AR): ML enhances AR applications by accurately overlaying virtual objects on real-world scenes, improving user interaction and experience.

Machine learning’s ability to analyze and interpret visual data continues to drive innovation across various fields, making computer vision a pivotal technology in the modern world.