Computer Vision

Computer Vision is the field of artificial intelligence that enables machines to interpret and act on visual data—images, video, depth maps—much like humans do. Using convolutional and Transformer-based neural networks, it performs tasks such as image classification, object detection, semantic segmentation, pose estimation, and optical character recognition. A typical pipeline ingests raw pixels, normalizes them, extracts hierarchical features, and outputs bounding boxes, labels, or keypoints. Pretrained models like ResNet, YOLOv8, and Vision Transformer (ViT) achieve real-time performance with GPU acceleration, while edge-optimized variants run on smartphones and IoT cameras. Training relies on large labeled datasets (ImageNet, COCO) augmented with synthetic images, and evaluation uses metrics like mAP, IoU, and F1. Applications span autonomous driving, medical imaging, retail analytics, and multimodal Retrieval-Augmented Generation (RAG) where visual context grounds an LLM’s response. Challenges include domain shift, bias, and privacy, addressed by domain adaptation, fairness audits, and on-device processing.