ComputerVisionAnwendungen

Computer vision is a field of study and engineering that enables machines to interpret visual information from the world, including images and video. It aims to replicate, automate, or augment aspects of human vision, from recognizing objects to understanding scenes and motion.

Modern computer vision relies largely on machine learning, especially deep learning, to learn representations from data. Traditional approaches used hand-crafted features such as SIFT or HOG and rule-based pipelines, but deep neural networks trained on large datasets have achieved state-of-the-art performance on many tasks.

Common tasks include image classification, object detection, semantic and instance segmentation, optical flow, 3D reconstruction, and video tracking. Applications span autonomous vehicles, medical imaging, surveillance and security, robotics, manufacturing, agriculture, and augmented reality.

Key datasets include ImageNet, COCO, PASCAL VOC, KITTI, Cityscapes. Evaluation uses metrics like accuracy, mean average precision (mAP), and intersection over union (IoU). The field faces challenges such as data bias, variability in lighting and viewpoint, occlusion, real-time processing requirements, and robustness to adversarial inputs.

Emerging areas include self-supervised learning, multimodal vision integrating language, and 3D vision. The field remains rapidly evolving, intersecting with artificial intelligence, computer graphics, and robotics.