Robust estimation of image motion by the fusion of gradient-based and feature-matching methods

1994 ◽  
Author(s):  
Kwangho Lee ◽  
KwangYun Wohn
Keyword(s):  
Robust Estimation ◽  
Feature Matching ◽  
Image Motion ◽  
Matching Methods ◽  
Gradient Based

scholarly journals A ROBUST ESTIMATION TECHNIQUE FOR 3D POINT CLOUD REGISTRATION

2016 ◽  
Vol 35 (1) ◽  
pp. 15 ◽  
Author(s):  
Dhanya S Pankaj ◽  
Rama Rao Nidamanuri
Keyword(s):  
Robust Estimation ◽  
Point Cloud ◽  
Feature Matching ◽  
Computational Time ◽  
Model Estimation ◽  
3D Registration ◽  
3D Images ◽  
Data Points ◽  
Pairwise Registration ◽  
3D Scans

The 3D modeling pipeline involves registration of partially overlapping 3D scans of an object. The automatic pairwise coarse alignment of partially overlapping 3D images is generally performed using 3D feature matching. The transformation estimation from matched features generally requires robust estimation due to the presence of outliers. RANSAC is a method of choice in problems where model estimation is to be done from data samples containing outliers. The number of RANSAC iterations depends on the number of data points and inliers to the model. Convergence of RANSAC can be very slow in the case of large number of outliers. This paper presents a novel algorithm for the 3D registration task which provides more accurate results in lesser computational time compared to RANSAC. The proposed algorithm is also compared against the existing modifications of RANSAC for 3D pairwise registration. The results indicate that the proposed algorithm tends to obtain the best 3D transformation matrix in lesser time compared to the other algorithms.

scholarly journals Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods

2021 ◽  
Vol 10 (11) ◽  
pp. 748
Author(s):  
Ferdinand Maiwald ◽  
Christoph Lehmann ◽  
Taras Lazariv
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Image Retrieval ◽  
Pose Estimation ◽  
Large Scale ◽  
Feature Matching ◽  
Geographic Information ◽  
Content Based Image Retrieval ◽  
Matching Methods ◽  
Historical Images

The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

Gradient-based optical flow techniques for tracking image motion due to atmospheric turbulence

2001 ◽  
Author(s):  
Ian Scott-Fleming ◽  
Keith Hege ◽  
David Clyde ◽  
Donald Fraser ◽  
Andrew Lambert
Keyword(s):  
Optical Flow ◽  
Atmospheric Turbulence ◽  
Image Motion ◽  
Gradient Based ◽  
Flow Techniques

Residual-Based robust estimation and image-motion analysis

1990 ◽  
Vol 2 (4) ◽  
pp. 371-379 ◽  
Author(s):  
Xinhua Zhuang ◽  
Yunxin Zhao ◽  
Thomas S. Huang
Keyword(s):  
Motion Analysis ◽  
Robust Estimation ◽  
Image Motion ◽  
Image Motion Analysis

scholarly journals Robust Gradient-Based Markov Subsampling

2020 ◽  
Vol 34 (04) ◽  
pp. 4004-4011
Author(s):  
Tieliang Gong ◽  
Quanhan Xi ◽  
Chen Xu
Keyword(s):  
Big Data ◽  
Robust Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
Initial Estimate ◽  
High Importance ◽  
The Core ◽  
Gradient Based ◽  
Core Idea ◽  
True Signal

Subsampling is a widely used and effective method to deal with the challenges brought by big data. Most subsampling procedures are designed based on the importance sampling framework, where samples with high importance measures are given corresponding sampling probabilities. However, in the highly noisy case, these samples may cause an unstable estimator which could lead to a misleading result. To tackle this issue, we propose a gradient-based Markov subsampling (GMS) algorithm to achieve robust estimation. The core idea is to construct a subset which allows us to conservatively correct a crude initial estimate towards the true signal. Specifically, GMS selects samples with small gradients via a probabilistic procedure, constructing a subset that is likely to exclude noisy samples and provide a safe improvement over the initial estimate. We show that the GMS estimator is statistically consistent at a rate which matches the optimal in the minimax sense. The promising performance of GMS is supported by simulation studies and real data examples.

scholarly journals ROBUST ESTIMATION IN ROBOT VISION AND PHOTOGRAMMETRY: A NEW MODEL AND ITS APPLICATIONS

2021 ◽  
Vol V-1-2021 ◽  
pp. 137-144
Author(s):  
J. Li ◽  
Y. Zhang ◽  
Q. Hu
Keyword(s):  
Least Squares ◽  
Robust Estimation ◽  
Control Parameter ◽  
Feature Matching ◽  
Geometric Model ◽  
Robot Vision ◽  
Approximate Solutions ◽  
Scan Matching ◽  
Proposed Model ◽  
Image Orientation

Abstract. Robust estimation (RE) is a fundamental issue in robot vision and photogrammetry, which is the theoretical basis of geometric model estimation with outliers. However, M-estimations solved by iteratively reweighted least squares (IRLS) are only suitable for cases with low outlier rates (< 50%); random sample consensus (RANSAC) can only obtain approximate solutions. In this paper, we propose an accurate and general RE model that unifies various robust costs into a common objective function by introducing a “robustness-control” parameter. It is a superset of typical least-squares, l1-l2, Cauchy, and Geman-McClure estimates. We introduce a parameter-decreasing strategy into the IRLS to optimize our model, called adaptive IRLS. The adaptive IRLS begins with a least-squares estimate for initialization. Then, the “robustness-control” parameter is decreased along with iterations so that the proposed model acts as different robust loss functions and has different degrees of robustness. We also apply the proposed model in several important tasks of robot vision and photogrammetry, such as line fitting, feature matching, image orientation, and point cloud registration (scan matching). Extensive simulated and real experiments show that the proposed model is robust to more than 80% outliers and preserves the advantages of M-estimations (fast and optimal). Our source code will be made publicly available in https://ljy-rs.github.io/web.

Word-Finding Support From Mobile Technology Benefits a Woman With Aphasia

2015 ◽  
Vol 24 (1) ◽  
pp. 26-39 ◽  
Author(s):  
Yvonne Gillette
Keyword(s):  
Mobile Technology ◽  
Clinical Education ◽  
Augmentative And Alternative Communication ◽  
Feature Matching ◽  
Mobile Technologies ◽  
Alternative Communication ◽  
Alternative Intervention ◽  
Communication Assessment ◽  
Word Finding ◽  
Intervention Costs

Mobile technology provides a solution for individuals who require augmentative and alternative intervention. Principles of augmentative and alternative communication assessment and intervention, such as feature matching and the participation model, developed with dedicated speech-generating devices can be applied to these generic mobile technologies with success. This article presents a clinical review of an adult with aphasia who reached her goals for greater communicative participation through mobile technology. Details presented include device selection, sequence of intervention, and funding issues related to device purchase and intervention costs. Issues related to graduate student clinical education are addressed. The purpose of the article is to encourage clinicians to consider mobile technology when intervening with an individual diagnosed with mild receptive and moderate expressive aphasia featuring word-finding difficulties.

Robust Estimation of Integrated Hydraulics and Parameters in Water Distribution Systems

2001 ◽  
Author(s):  
Mietek A. Brdys ◽  
Kazimierz Duzinkiewicz ◽  
Michal Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Robust Estimation ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems
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