What are the different subfields in computer vision?

Computer vision encompasses several subfields focused on enabling machines to interpret visual data. These areas address distinct challenges, from recognizing objects in images to reconstructing 3D environments. Below, I’ll outline key subfields, their goals, and practical applications.

Core Recognition Tasks Image classification, object detection, and segmentation form the foundation. Classification identifies the primary subject in an image (e.g., labeling a photo as “cat” or “dog” using models like ResNet). Object detection locates and classifies multiple objects within an image, often using bounding boxes—tools like YOLO or Faster R-CNN are common here. Segmentation goes further by labeling each pixel, distinguishing object boundaries. For example, U-Net is widely used in medical imaging to outline tumors in MRI scans. These tasks are critical for applications like content moderation or autonomous driving.

Scene Understanding and Reconstruction Subfields like 3D reconstruction and optical flow analyze spatial and temporal relationships. 3D reconstruction builds models of environments from 2D images, using techniques like structure-from-motion (SfM) or SLAM (Simultaneous Localization and Mapping), which help robots navigate. Optical flow estimates motion between video frames, useful for tracking vehicles in traffic analysis. Feature extraction, another key area, identifies unique points (e.g., SIFT or ORB features) to match objects across images, enabling applications like panorama stitching or augmented reality (AR) overlays.

Specialized Applications Some subfields target specific domains. Facial recognition systems verify identities by analyzing features like eye spacing or jawline, used in smartphone authentication. Medical imaging focuses on enhancing diagnostics—for instance, detecting retinal damage in diabetic patients. Autonomous vehicles combine multiple subfields: object detection avoids obstacles, while semantic segmentation categorizes road surfaces. Another example is anomaly detection in manufacturing, where vision systems spot defects on production lines. These specialized areas often integrate custom models tailored to unique datasets or hardware constraints.

Each subfield addresses specific technical challenges, but they frequently overlap in real-world systems. For example, a self-driving car might use detection, segmentation, and 3D mapping simultaneously. Understanding these areas helps developers choose the right tools for their projects.