Computer Vision is a field of Artificial Intelligence that trains computers to interpret and understand the visual world. It involves extracting meaningful information from images or videos. In this section, we will explore the basics of Machine Learning applied to Computer Vision.
Key Concepts
- Image Processing: The initial step in Computer Vision where raw images are processed to extract useful features.
- Feature Extraction: Techniques to transform raw data into a format that is suitable for further analysis.
- Machine Learning Models: Algorithms that learn from data to make predictions or decisions.
Common Applications
- Object Detection: Identifying and locating objects within an image or video.
- Image Classification: Categorizing images into predefined classes.
- Image Segmentation: Dividing an image into multiple segments or regions.
Learning Resources
To dive deeper into the world of Machine Learning in Computer Vision, here are some valuable resources:
- Introduction to Computer Vision
- Deep Learning for Computer Vision
- Practical Projects in Computer Vision
Images in Computer Vision
Computer Vision is all about seeing the world through the eyes of a machine. Let's take a look at some images processed using Computer Vision techniques:
In this image, objects are detected and outlined, showcasing the power of Computer Vision in identifying and localizing objects within an image.
If you're interested in exploring more about Computer Vision and Machine Learning, don't forget to check out our tutorials and projects. Happy learning!