Computer Vision is a field of artificial intelligence that focuses on enabling computers to interpret and understand the visual world. It involves analyzing and processing images and videos to extract useful information and insights. Computer Vision has a wide range of applications such as self-driving cars, facial recognition, medical imaging, and more.
Image processing is a fundamental concept in Computer Vision. It involves manipulating and enhancing digital images to extract useful information. The process involves several steps including image acquisition, preprocessing, segmentation, feature extraction, and classification.
Convolutional Neural Networks (CNNs) are a type of artificial neural network that are widely used in Computer Vision. They are designed to process and analyze visual data such as images and videos. CNNs are made up of several layers including convolutional layers, pooling layers, and fully connected layers. These layers work together to extract features from the input data and make predictions.
Object detection is the process of identifying and localizing objects within an image or video. It involves using computer algorithms to identify specific patterns and features within the input data. Object detection has many applications such as surveillance, robotics, and more.
Facial recognition is a type of biometric technology that uses Computer Vision to identify and verify an individual's identity based on their facial features. It involves comparing a person's facial features to a database of known faces to make a match. Facial recognition has many applications such as security, law enforcement, and more.
Computer Vision is a rapidly growing field of artificial intelligence with many practical applications. It involves analyzing and processing visual data to extract useful information and insights. Convolutional Neural Networks, object detection, and facial recognition are just a few examples of the many techniques used in Computer Vision.