Random Sample Consensus: Robust Estimation in Computer Vision

What is Random Sample Consensus

Random sample consensus, also known as RANSAC, is an iterative method that is used to estimate the parameters of a mathematical model based on a collection of observed data that includes outliers. This method is used in situations where the outliers are permitted to have no impact on the values of the estimates. The conclusion is that it is also possible to view it as a tool for detecting outliers. An algorithm is considered to be non-deterministic if it is able to generate a suitable result only with a certain probability, and this likelihood increases as the number of iterations that are permitted via the method increases. In 1981, Fischler and Bolles, who were working at SRI International, were the ones who initially published the algorithm. In order to solve the Location Determination Problem (LDP), which is a problem in which the objective is to find the points in space that project onto an image and then convert those points into a set of landmarks with known positions, they utilized RANSAC.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Random sample consensus

Chapter 2: Estimator

Chapter 3: Least squares

Chapter 4: Outlier

Chapter 5: Cross-validation (statistics)

Chapter 6: Errors and residuals

Chapter 7: Mixture model

Chapter 8: Robust statistics

Chapter 9: Image stitching

Chapter 10: Resampling (statistics)

(II) Answering the public top questions about random sample consensus.

(III) Real world examples for the usage of random sample consensus 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 Random Sample Consensus.