scholarly journals Complex Objects Pose Estimation based on Image Moment Invariants

2006 ◽  
Author(s):  
O. Tahri ◽  
F. Chaumette
Keyword(s):  
Pose Estimation ◽  
Moment Invariants ◽  
Complex Objects ◽  
Image Moment ◽  
Image Moment Invariants

Orthogonal Image Moment Invariants

2012 ◽  
pp. 34-52 ◽  
Author(s):  
G.A. Papakostas ◽  
E.G. Karakasis ◽  
D.E. Koulouriotis
Keyword(s):  
Pattern Recognition ◽  
Benchmark Problems ◽  
Moment Invariants ◽  
Orthogonal Moments ◽  
Image Moment ◽  
Image Moment Invariants

This chapter focuses on the usage of image orthogonal moments as discrimination features in pattern recognition applications and discusses their main properties. Initially, the ability of the moments to carry information of an image with minimum redundancy is studied, while their capability to enclose distinctive information that uniquely describes the image’s content is also examined. Along these directions, the computational formulas of the most representative moment families will be defined analytically and the form of the corresponding moment invariants in each case will be derived. Appropriate experiments have taken place in order to investigate the description capabilities of each moment family, by applying them in several benchmark problems.

Orthogonal Image Moment Invariants

2013 ◽  
pp. 15-32
Author(s):  
G.A. Papakostas ◽  
E.G. Karakasis ◽  
D.E. Koulouriotis
Keyword(s):  
Pattern Recognition ◽  
Benchmark Problems ◽  
Moment Invariants ◽  
Orthogonal Moments ◽  
Image Moment ◽  
Image Moment Invariants

This chapter focuses on the usage of image orthogonal moments as discrimination features in pattern recognition applications and discusses their main properties. Initially, the ability of the moments to carry information of an image with minimum redundancy is studied, while their capability to enclose distinctive information that uniquely describes the image’s content is also examined. Along these directions, the computational formulas of the most representative moment families will be defined analytically and the form of the corresponding moment invariants in each case will be derived. Appropriate experiments have taken place in order to investigate the description capabilities of each moment family, by applying them in several benchmark problems.

scholarly journals Principal Component Analysis For ICP Pose Estimation Of Space Structures

2021 ◽  
Author(s):  
Lun H. Mark
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Optimal Combination ◽  
Estimation Accuracy ◽  
Space Structures ◽  
Complex Objects ◽  
Error Norm ◽  
Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

scholarly journals Principal Component Analysis For ICP Pose Estimation Of Space Structures

2021 ◽  
Author(s):  
Lun H. Mark
Keyword(s):  
Pose Estimation ◽  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Optimal Combination ◽  
Estimation Accuracy ◽  
Space Structures ◽  
Complex Objects ◽  
Error Norm ◽  
Simulation Results

This thesis investigates how geometry of complex objects is related to LIDAR scanning with the Iterative Closest Point (ICP) pose estimation and provides statistical means to assess the pose accuracy. LIDAR scanners have become essential parts of space vision systems for autonomous docking and rendezvous. Principal Componenet Analysis based geometric constraint indices have been found to be strongly related to the pose error norm and the error of each individual degree of freedom. This leads to the development of several strategies for identifying the best view of an object and the optimal combination of localized scanned areas of the object's surface to achieve accurate pose estimation. Also investigated is the possible relation between the ICP pose estimation accuracy and the districution or allocation of the point cloud. The simulation results were validated using point clouds generated by scanning models of Quicksat and a cuboctahedron using Neptec's TriDAR scanner.

Localisation method for a Mars aircraft using multispectral images

2018 ◽  
Vol 122 (1253) ◽  
pp. 1083-1101
Author(s):  
H. Tokutake ◽  
M. Kido
Keyword(s):  
Computational Complexity ◽  
Numerical Simulations ◽  
Working Group ◽  
Image Noise ◽  
Estimation Accuracy ◽  
Moment Invariants ◽  
Multispectral Images ◽  
Low Computational Complexity ◽  
Image Moment ◽  
Simple Mapping

ABSTRACTIn Japan, the working group for the Mars Exploration Aircraft continues to research and develop a Mars aircraft aiming to a future survey mission. To ensure the success of the flight mission, a self-localisation system with low-computational complexity is necessary. In the present research, a new self-localisation method is proposed using multispectral images. The algorithm is based on the simple mapping of image moment invariants and gradients calculated from several images at different wavelengths. The numerical simulations revealed the sufficient robustness of the proposed method to image noise. The estimation accuracy can be improved by increasing the number of the spectral images.

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