Description: Grain analysis is a technique used to examine the texture of images, which has become fundamental in the field of anomaly detection using artificial intelligence. This technique is based on identifying and evaluating patterns in the texture of images, allowing AI algorithms to discern between what is considered normal and what is considered anomalous. Through grain feature extraction, subtle variations that might go unnoticed can be detected. This is especially relevant in applications where precision is critical, such as quality inspection in manufacturing, medical image analysis, and security surveillance. Grain analysis not only enhances AI systems’ ability to identify issues but also optimizes decision-making processes by providing detailed information about the quality and integrity of visual data. In summary, grain analysis is a powerful tool that combines computer vision and machine learning to improve anomaly detection across various industries.
