Evaluation of Stochastic Gradient Descent Methods for Nonlinear Mapping of Hyperspectral Data

2016 ◽  
pp. 276-283 ◽  
Author(s):  
Evgeny Myasnikov
Keyword(s):  
Gradient Descent ◽  
Hyperspectral Data ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Descent Methods ◽  
Nonlinear Mapping ◽  
Gradient Descent Methods

scholarly journals Efficient 3D Shape Registration by Using Distance Maps and Stochastic Gradient Descent Method

2020 ◽  
Vol 75 (1) ◽  
pp. 81-102
Author(s):  
Polycarp Omondi Okock ◽  
Jozef Urbán ◽  
Karol Mikula
Keyword(s):  
Level Sets ◽  
Gradient Descent ◽  
Descent Method ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
3D Shape ◽  
Shape Registration ◽  
Gradient Descent Methods ◽  
Speed Up

AbstractThis paper presents an efficient 3D shape registration by using distance maps and stochastic gradient descent method. The proposed algorithm aims to find the optimal affine transformation parameters (translation, scaling and rotation) that maps two distance maps to each other. These distance maps represent the shapes as an interface and we apply level sets methods to calculate the signed distance to these interfaces. To maximize the similarity between the two distance maps, we apply sum of squared difference (SSD) optimization and gradient descent methods to minimize it. To address the shortcomings of the standard gradient descent method, i.e., many iterations to compute the minimum, we implemented the stochastic gradient descent method. The outcome of these two methods are compared to show the advantages of using stochastic gradient descent method. In addition, we implement computational optimization’s such as parallelization to speed up the registration process.

Sign in / Sign up

Export Citation Format

Share Document