This book discusses mathematical foundations of statistical inference for building a 3-D model of the environment from image and sensor data that contain noise - a central task for autonomous robots guided by video cameras and sensors. A theoretical accuracy bound is derived for the optimization pro
Statistical Optimization for Geometric Computation: Theory and Practice
โ Scribed by Kenichi Kanatani
- Publisher
- Dover Publications
- Year
- 2005
- Tongue
- English
- Leaves
- 508
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
This text discusses the mathematical foundations of statistical inference for building 3-dimensional models from image and sensor data that contain noise - a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. 1996 edition.
๐ SIMILAR VOLUMES
This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using
This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using
<b>A UNIQUE ENGINEERING AND STATISTICAL APPROACH TO OPTIMAL RESOURCE ALLOCATION</b><p><i>Optimal Resource Allocation: With Practical Statistical Applications and Theory </i>features the application of probabilistic and statistical methods used in reliability engineering during the different phases o