Particle Filter - Exploring Particle Filters in Computer Vision
Fouad Sabry
Publisher: One Billion Knowledgeable
Summary
What is Particle Filter Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Pierre Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Jun S. Liu and Rong Chen in 1998. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Particle filter Chapter 2: Importance sampling Chapter 3: Point process Chapter 4: Fokker-Planck equation Chapter 5: Wiener's lemma Chapter 6: Klein-Kramers equation Chapter 7: Mean-field particle methods Chapter 8: Dirichlet kernel Chapter 9: Generalized Pareto distribution Chapter 10: Superprocess (II) Answering the public top questions about particle filter. (III) Real world examples for the usage of particle filter in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Particle Filter.