Course Content :-

• Two-dimensional visual geometry :-

2d transformation family. The homograph. Estimating 2d transformations. Image panoramas.

• 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 color 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 :-

Modeling covariance 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.