scholarly journals Groupwise Registration via Graph Shrinkage on the Image Manifold

2013 ◽  
Author(s):  
Shihui Ying ◽  
Guorong Wu ◽  
Qian Wang ◽  
Dinggang Shen
Keyword(s):  
Groupwise Registration ◽  
Image Manifold

scholarly journals Fast Groupwise Registration Using Multi-Level and Multi-Resolution Graph Shrinkage

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pei Dong ◽  
Xiaohuan Cao ◽  
Pew-Thian Yap ◽  
Dinggang Shen
Keyword(s):  
Resolution Graph ◽  
Multi Level ◽  
Groupwise Registration

scholarly journals Generative Visual Manipulation on the Natural Image Manifold

2016 ◽  
pp. 597-613 ◽  
Author(s):  
Jun-Yan Zhu ◽  
Philipp Krähenbühl ◽  
Eli Shechtman ◽  
Alexei A. Efros
Keyword(s):  
Natural Image ◽  
Image Manifold

scholarly journals Hubless keypoint-based 3D deformable groupwise registration

2020 ◽  
Vol 59 ◽  
pp. 101564
Author(s):  
R. Agier ◽  
S. Valette ◽  
R. Kéchichian ◽  
L. Fanton ◽  
R. Prost
Keyword(s):  
Groupwise Registration

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

2013 ◽  
Vol 756-759 ◽  
pp. 4121-4125
Author(s):  
Peng Zhang ◽  
Yuan Yuan Ren
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
Target Recognition ◽  
Perceptual Information ◽  
System Control ◽  
Aerial Vehicles ◽  
Robust Feature Extraction ◽  
Image Manifold ◽  
Low Dimensional ◽  
Low Dimensional Features

Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted low-dimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.

A New Extension of Locality Preserving Projections for Image Feature Extraction

2012 ◽  
Vol 241-244 ◽  
pp. 1715-1718
Author(s):  
Guo Hong Huang
Keyword(s):  
Feature Extraction ◽  
Local Information ◽  
Image Feature ◽  
Two Dimensional ◽  
Two Stage ◽  
Image Feature Extraction ◽  
Locality Preserving ◽  
Image Manifold ◽  
New Framework ◽  
Novel Algorithm

This paper proposes a novel algorithm for image feature extraction, namely, the two-directional two-dimensional locality preserving projection, ((2D)2LPP), which can find an embedding from two directions that not only preserves local information and detect the intrinsic image manifold structure, but also combines the both information between rows and those between columns simultaneously. We also combine the advantages of (2D)2LPP and LDA, and propose a new framework for feature extraction as two-stage: “(2D)2LPP+LDA.” The LDA step is performed to further reduce the dimension of feature matrix in the (2D)2LPP subspace. Experimental results on ORL face databases demonstrate the effectiveness of the proposed methods.

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