5. AI Fundamentals: Computer Vision
Computer vision is a field of artificial intelligence (AI) that gives computers the ability to see and understand the world around them. It is used in a wide variety of applications, such as self-driving cars, facial recognition, and image search.
Computer vision systems typically consist of three main components:
Image acquisition: This is the process of capturing an image or video from the real world.
Image processing: This is the process of transforming the image or video into a format that can be understood by the computer. This includes tasks such as noise removal, edge detection, and feature extraction.
Image understanding: This is the process of interpreting the image or video and extracting meaning from it. This includes tasks such as object detection, classification, and tracking.
Computer vision algorithms are typically trained on large datasets of labeled data. The labels tell the algorithm what the correct output should be for each input. The algorithm then learns to map from inputs to outputs by adjusting its parameters.
There are many different types of computer vision algorithms, each with its own strengths and weaknesses. Some common types of computer vision algorithms include:
Convolutional neural networks (CNNs): CNNs are a type of deep learning algorithm that are particularly well-suited for image processing tasks. They are able to learn to identify patterns in images, even in the presence of noise.
Support vector machines (SVMs): SVMs are a type of machine learning algorithm that can be used for classification and regression tasks. They are able to learn to separate different classes of data with a clear margin.
Random forests: Random forests are a type of ensemble learning algorithm that can be used for classification and regression tasks. They are able to learn to identify patterns in data by combining the predictions of multiple decision trees.
Computer vision is a rapidly growing field, and it is expected to have a major impact on our lives in the years to come. Computer vision has the potential to solve some of the world's most pressing problems, such as climate change, poverty, and disease. However, computer vision also raises some ethical concerns, such as the potential for job displacement and the misuse of computer vision for malicious purposes.
