Computer vision (CV) and machine learning (ML) algorithms are essential for processing and understanding visual data. CV algorithms include techniques such as edge detection, image segmentation, and feature extraction. These methods help analyze and preprocess images for further processing. For instance, edge detection identifies object boundaries, enabling applications like defect detection and shape recognition in manufacturing.
ML algorithms, especially deep learning models, bring advanced capabilities to CV systems. Convolutional Neural Networks (CNNs) are widely used for tasks such as object detection and image classification. For example, a CNN can classify products or detect anomalies on an assembly line. Additionally, algorithms like Generative Adversarial Networks (GANs) are employed to generate synthetic images for training or enhance image quality. These algorithms improve the performance and versatility of CV applications.
Hybrid approaches combine traditional CV techniques with ML models for more complex tasks. For example, an autonomous vehicle might use optical flow algorithms to estimate motion while relying on a CNN to detect pedestrians and traffic signs. Together, these algorithms enable advanced applications like medical imaging, automated inspections, and augmented reality, empowering machines to make sense of the visual world with precision and scalability.