Probabilistic Algorithms for Computer Vision

Mathematical models of images and objects used to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.

Content:

Two-dimensional visual geometry:

2-D transformation family. The homography. Estimating 2-D transformations. Image panoramas.

Three dimensional image geometry:

The projective camera. Camera calibration. Recovering pose to a plane.

More than one camera:

The fundamental and essential matrices. Sparse stereo methods. Rectification. Building 3D models. Shape from silhouette.

Vision at a single pixel:

Background subtraction and colour segmentations problems. Parametric, non-parametric and semi-parametric techniques. Fitting models with hidden variables.

Connecting pixels:

Dynamic programming for stereo vision. Markov random fields. MCMC methods. Graph cuts.

Texture:

Texture synthesis, super-resolution and denoising, image inpainting. The epitome of an image.

Dense Object Recognition:

Modelling covariances of pixel regions. Factor analysis and principle components analysis.

Sparse Object Recognition:

Bag of words, latent dirilecht allocation, probabilistic latent semantic analysis.

Face Recognition:

Probabilistic approaches to identity recognition. Face recognition in disparate viewing conditions.

Shape Analysis:

Point distribution models, active shape models, active appearance models.

Tracking:

The Kalman filter, the Condensation algorithm.