Rotation Invariant Co-occurrence among Adjacent LBPs

2013 ◽  
pp. 15-25 ◽  
Author(s):  
Ryusuke Nosaka ◽  
Chendra Hadi Suryanto ◽  
Kazuhiro Fukui
Keyword(s):  
Rotation Invariant

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

scholarly journals HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor

2021 ◽  
Vol 13 (12) ◽  
pp. 2328
Author(s):  
Yameng Hong ◽  
Chengcai Leng ◽  
Xinyue Zhang ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Image Registration ◽  
Local Binary Pattern ◽  
Computational Time ◽  
Rotation Invariant ◽  
Edge Information ◽  
Important Research Topic ◽  
New Construction ◽  
Novel Method ◽  
Direction Information ◽  
Angle Calculation

Image registration has always been an important research topic. This paper proposes a novel method of constructing descriptors called the histogram of oriented local binary pattern descriptor (HOLBP) for fast and robust matching. There are three new components in our algorithm. First, we redefined the gradient and angle calculation template to make it more sensitive to edge information. Second, we proposed a new construction method of the HOLBP descriptor and improved the traditional local binary pattern (LBP) computation template. Third, the principle of uniform rotation-invariant LBP was applied to add 10-dimensional gradient direction information to form a 138-dimension HOLBP descriptor vector. The experimental results showed that our method is very stable in terms of accuracy and computational time for different test images.

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