This document discusses several techniques for image-based rendering including light field rendering, the lumigraph, view-dependent texture mapping, the unstructured lumigraph, blending fields, and unstructured light fields. It provides intuitive explanations of each technique and how they represent scenes and allow novel views to be rendered from different positions and angles.
The document describes the mean shift algorithm and its application to object tracking in computer vision. Mean shift is an iterative procedure that moves data points to the average of nearby points, converging at modes of the data's probability density function. It can be used for tracking by modeling a target object's color distribution and applying mean shift to match candidate locations in subsequent frames. The algorithm maximizes the Bhattacharyya coefficient between color distributions to find the best match for the target's new location in each frame.
1) Kernel Bayes' rule provides a nonparametric approach to Bayesian inference using positive definite kernels. It represents probabilities as elements in a reproducing kernel Hilbert space.
2) Using kernel mean embeddings, kernel Bayes' rule computes the posterior kernel mean directly from covariance operators without needing to compute integrals or approximations.
3) Given samples from the joint distribution and the prior kernel mean, kernel Bayes' rule computes the posterior kernel mean as a weighted sum of prior sample kernel embeddings, providing a nonparametric realization of Bayesian inference.