Computer vision is a field of artificial intelligence that enables machines to interpret and analyze visual data, such as images or videos. It combines techniques from image processing, pattern recognition, and machine learning to extract meaningful information from visual inputs. At its core, computer vision systems use algorithms to identify edges, shapes, textures, or objects within an image, often relying on neural networks like convolutional neural networks (CNNs) to process pixel data hierarchically. For example, a basic task might involve detecting faces in a photo using Haar cascades or recognizing handwritten digits with a trained model like MNIST.
Applications of computer vision span numerous industries. In healthcare, it assists in analyzing medical imagery, such as identifying tumors in X-rays or MRI scans. Autonomous vehicles rely on real-time object detection to navigate roads, using cameras and LiDAR to distinguish pedestrians, traffic signs, and other vehicles. Retail uses computer vision for inventory management—cameras monitor shelf stock levels, while checkout systems employ image recognition to identify products without barcodes. Another example is agriculture, where drones equipped with cameras monitor crop health by detecting disease patterns in aerial imagery. These applications often combine computer vision with other technologies, such as sensor fusion in self-driving cars or cloud computing for scalable image analysis.
Developers working with computer vision typically use frameworks like OpenCV, TensorFlow, or PyTorch to build models. Pre-trained models, such as YOLO (You Only Look Once) for object detection or ResNet for image classification, provide starting points that can be fine-tuned for specific tasks. Challenges include handling variations in lighting, occlusion, or perspective in images, as well as optimizing models for real-time performance. For instance, a developer might deploy a model on edge devices using TensorFlow Lite to reduce latency. As the field advances, integrating domain-specific knowledge—like understanding medical terminology for healthcare applications—becomes critical for creating effective solutions.