Computer Vision, Explained.

Image Processing & Computational Photography

Computing each output pixel from a neighborhood of input pixels, the family of operations behind smoothing, sharpening, edge detection, and denoising.

Features & Representation

Finding distinctive, repeatable points and regions in an image, such as corners, blobs, and stable regions, so that the same scene locations can be found again in other images.

Recognition & Detection

Assigning one or more category labels to a whole image, usually by predicting a probability for each of a fixed set of classes.

Recognition & Detection

Finding every object of a set of categories in an image and returning, for each one, a bounding box, a class label and a confidence score.

Segmentation

Dividing an image into regions by assigning every pixel to a segment, a class, or an object instance, the task behind semantic, instance, panoptic and interactive segmentation.

Deep Learning for Vision

A neural network built from convolutional layers, small learned filters applied at every image position, interleaved with nonlinearities and downsampling, so that features grow from edges to object parts while the parameter count stays independent of image size.

Motion & Tracking

The apparent motion of image content between two frames, represented as a two-dimensional displacement at every pixel.

Geometry & 3D Vision

Estimating a camera's intrinsic parameters, lens distortion, and pose from images of a target with known geometry, usually by a closed-form initialization followed by minimizing reprojection error.

Geometry & 3D Vision

Recovering the poses and intrinsics of the cameras that took a set of images, together with a sparse 3D point cloud of the scene, from the images alone, by incremental or global reconstruction.

Motion & Tracking

Estimating the trajectories of a varying, unknown number of objects in a video while keeping each object's identity consistent over time.