scholarly journals Rotation-invariant observables as density matrix invariants

2019 ◽  
Vol 99 (7) ◽  
Author(s):  
Margarita Gavrilova ◽  
Oleg Teryaev
Keyword(s):  
Density Matrix ◽  
Rotation Invariant ◽  
Matrix Invariants

Density matrix and rate equation analyses for picosecond pump/probe combustion diagnostics

AIAA Journal ◽  
1999 ◽  
Vol 37 ◽  
pp. 723-731
Author(s):  
Thomas Settersten ◽  
Mark Linne ◽  
James Gord ◽  
Gregory Feichtner
Keyword(s):  
Density Matrix ◽  
Rate Equation ◽  
Pump Probe ◽  
Combustion Diagnostics

Rotation Invariant Neural Network-Based Face Detection

1997 ◽  
Author(s):  
Henry A. Rowley ◽  
Shumeet Baluja ◽  
Takeo Kanade
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Rotation Invariant

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

LFNet: Local Rotation Invariant Coordinates Frame for Robust Point Cloud Analysis

2021 ◽  
pp. 1-1
Author(s):  
Hezhi Cao ◽  
Ronghui Zhan ◽  
Yanxin Ma ◽  
Chao Ma ◽  
Jun Zhang
Keyword(s):  
Point Cloud ◽  
Rotation Invariant ◽  
Local Rotation ◽  
Point Cloud Analysis ◽  
Cloud Analysis
Sign in / Sign up

Export Citation Format

Share Document