Computer Vision

Computer vision is a field of artificial intelligence that trains machines to interpret and understand visual information from the world, enabling applications ranging from quality inspection in manufacturing to autonomous navigation and medical image analysis. By processing images and video through neural networks, computer vision systems can detect objects, recognize faces, read text, segment scenes, estimate poses, and understand spatial relationships.

Modern computer vision is built primarily on convolutional neural networks (CNNs) and, increasingly, vision transformers (ViTs) that apply the attention mechanism from NLP to visual data. Key task categories include image classification (what is in the image), object detection (where specific items are located), semantic segmentation (pixel-level labeling of scene content), instance segmentation (distinguishing individual objects), and video understanding (temporal analysis of visual sequences).

Foundation models have transformed computer vision similarly to NLP. Models like CLIP enable zero-shot image classification by learning joint visual-text representations. The Segment Anything Model (SAM) provides universal image segmentation capabilities. Multimodal models like GPT-4V and Gemini can reason about images using natural language, answering questions about visual content, describing scenes, and extracting information from documents and charts.

Enterprise computer vision applications are deployed across industries: manufacturing uses vision for defect detection and quality control on production lines; retail applies vision for inventory management and customer analytics; healthcare uses medical imaging AI for diagnosis support; agriculture monitors crop health from aerial imagery; and security systems use vision for access control and threat detection. Edge deployment on platforms like NVIDIA Jetson enables real-time visual inference at the point of need, which is critical for applications requiring immediate response.