scholarly journals Fast Image Registration for Spacecraft Autonomous Navigation Using Natural Landmarks

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yun-Hua Wu ◽  
Lin-Lin Ge ◽  
Feng Wang ◽  
Bing Hua ◽  
Zhi-Ming Chen ◽  
...  
Keyword(s):  
Image Registration ◽  
Autonomous Navigation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Speeded Up Robust Features ◽  
Speed Up ◽  
Random Sample Consensus ◽  
Invariant Properties ◽  
Low Computational Cost ◽  
Ransac Algorithm

In order to satisfy the real-time requirement of spacecraft autonomous navigation using natural landmarks, a novel algorithm called CSA-SURF (chessboard segmentation algorithm and speeded up robust features) is proposed to improve the speed without loss of repeatability performance of image registration progress. It is a combination of chessboard segmentation algorithm and SURF. Here, SURF is used to extract the features from satellite images because of its scale- and rotation-invariant properties and low computational cost. CSA is based on image segmentation technology, aiming to find representative blocks, which will be allocated to different tasks to speed up the image registration progress. To illustrate the advantages of the proposed algorithm, PCA-SURF, which is the combination of principle component analysis and SURF, is also analyzed in this paper for comparison. Furthermore, random sample consensus (RANSAC) algorithm is applied to eliminate the false matches for further accuracy improvement. The simulation results show that the proposed strategy obtains good results, especially in scaling and rotation variation. Besides, CSA-SURF decreased 50% of the time in extraction and 90% of the time in matching without losing the repeatability performance by comparing with SURF algorithm. The proposed method has been demonstrated as an alternative way for image registration of spacecraft autonomous navigation using natural landmarks.

scholarly journals A New Generalized Projection and Its Application to Acceleration of Audio Declipping

Axioms ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 105
Author(s):  
Pavel Rajmic ◽  
Pavel Záviška ◽  
Vítězslav Veselý ◽  
Ondřej Mokrý
Keyword(s):  
Signal Processing ◽  
Convex Optimization ◽  
Linear Operator ◽  
Explicit Formula ◽  
Convex Sets ◽  
State Of The Art ◽  
Computational Cost ◽  
The Other ◽  
Speed Up ◽  
Low Computational Cost

In convex optimization, it is often inevitable to work with projectors onto convex sets composed with a linear operator. Such a need arises from both the theory and applications, with signal processing being a prominent and broad field where convex optimization has been used recently. In this article, a novel projector is presented, which generalizes previous results in that it admits to work with a broader family of linear transforms when compared with the state of the art but, on the other hand, it is limited to box-type convex sets in the transformed domain. The new projector is described by an explicit formula, which makes it simple to implement and requires a low computational cost. The projector is interpreted within the framework of the so-called proximal splitting theory. The convenience of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two.

scholarly journals A Time-Consistent Video Segmentation Algorithm Designed for Real-Time Implementation

VLSI Design ◽  
2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
M. El Hassani ◽  
S. Jehan-Besson ◽  
L. Brun ◽  
M. Revenu ◽  
M. Duranton ◽  
...  
Keyword(s):  
Real Time ◽  
Video Segmentation ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Motion Information ◽  
Temporal Consistency ◽  
Merging Process ◽  
Specific Order ◽  
Previous Frame ◽  
Low Computational Cost

We propose a time-consistent video segmentation algorithm designed for real-time implementation. Our algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images with few parameters to be set. Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved change-detection mask. This mask is designed using both illumination differences between frames and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.

Aerodynamic prediction of a projectile fitted with fins

2018 ◽  
Vol 28 (5) ◽  
pp. 1218-1236 ◽  
Author(s):  
Cédric Decrocq ◽  
Bastien Martinez ◽  
Marie Albisser ◽  
Simona Dobre ◽  
Patrick Gnemmi ◽  
...  
Keyword(s):  
Long Range ◽  
Computational Cost ◽  
Preliminary Design ◽  
Cfd Simulations ◽  
Content Type ◽  
Speed Up ◽  
Computational Fluid Dynamics Cfd ◽  
Lifting Surfaces ◽  
Time Accuracy ◽  
Low Computational Cost

Purpose The present paper deals with weapon aerodynamics and aims to describe preliminary studies that were conducted for developing the next generation of long-range guided ammunition. Over history, ballistic research scientists were constantly investigating new artillery systems capable of overcoming limitations of range, accuracy and manoeuvrability. While futuristic technologies are increasingly under development, numerous issues concerning current powdered systems still need to be addressed. In this context, the present work deals with the design and the optimization of a new concept of long-range projectile with regard to multidisciplinary fields, including flight scenario, steering strategy, mechanical actuators or size of payload. Design/methodology/approach Investigations are conducted for configurations that combine existing full calibre 155 mm guided artillery shell with a set of lifting surfaces. As the capability of the ammunition highly depends on lifting surfaces in terms of number, shape or position, a parametric study has to be conducted for determining the best aerodynamic architecture. To speed-up this process, initial estimations are conducted thanks to low computational cost methods suitable for preliminary design requirements, in terms of time, accuracy and flexibility. The WASP code (Wing-Aerodynamic-eStimation-for-Projectiles) has been developed for rapidly predicting aerodynamic coefficients (static and dynamic) of a set of lifting surfaces fitted on a projectile fuselage, as a function of geometry and flight conditions, up to transonic velocities. Findings In the present study, WASP predictions at Mach 0.7 of both normal force and pitching moment coefficients are assessed for two configurations. Originality/value Analysis is conducted by gathering results from WASP, computational-fluid-dynamics (CFD) simulations, wind-tunnel experiments and free-flight tests. Obtained results demonstrate the ability of WASP code to be used for preliminary design steps.

A Method for UAV’s Images Seamless Stitching

2014 ◽  
Vol 556-562 ◽  
pp. 5076-5080
Author(s):  
Peng Jun Li ◽  
Jian Zeng Li
Keyword(s):  
Random Sample ◽  
Nearest Neighbors ◽  
Experimental Results ◽  
Image Stitching ◽  
Feature Points ◽  
K Nearest Neighbors ◽  
Fusion Technique ◽  
Speeded Up Robust Features ◽  
Random Sample Consensus ◽  
Ransac Algorithm

Image stitching is an important technology to build a panorama image by combing several images with overlapped areas. In this study, we develop a image seamless mosaic and fusion technique to obtain a prefect panorama image after stitching. At first, it is usingspeeded-up robust features(SURF) algorithm to extract features form the images for stitching. Then,k-nearest neighbors(KNN) method is used to match the feature points andRandom sample consensus(RANSAC) algorithm is used to clear them. Thirdly, a method is improved to achieve seamless stitching based on optimal suture of the overlapped areas. Experimental results indicate that this method can eliminate cohesion gap of two stitching images very well.

scholarly journals Fast 3D Semantic Mapping on Naturalistic Road Scenes

2019 ◽  
Author(s):  
Xuanpeng Li ◽  
Dong Wang ◽  
Huanxuan Ao ◽  
Rachid Belaroussi ◽  
Dominique Gruyer
Keyword(s):  
Autonomous Navigation ◽  
Semantic Information ◽  
Reference Frames ◽  
Computational Cost ◽  
Semantic Segmentation ◽  
Semantic Mapping ◽  
Semantic Inference ◽  
Spatial Consistency ◽  
Mapping System ◽  
Speed Up

Fast 3D reconstruction with semantic information on road scenes is of great requirements for autonomous navigation. It involves issues of geometry and appearance in the field of computer vision. In this work, we propose a method of fast 3D semantic mapping based on the monocular vision. At present, due to the inexpensive price and easy installation, monocular cameras are widely equipped on recent vehicles for the advanced driver assistance and it is possible to acquire semantic information and 3D map. The monocular visual sequence is used to estimate the camera pose, calculate the depth, predict the semantic segmentation, and finally realize the 3D semantic mapping by combination of the techniques of localization, mapping and scene parsing. Our method recovers the 3D semantic mapping by incrementally transferring 2D semantic information to 3D point cloud. And a global optimization is explored to improve the accuracy of the semantic mapping in light of the spatial consistency. In our framework, there is no need to make semantic inference on each frame of the sequence, since the mesh data with semantic information is corresponding to sparse reference frames. It saves amounts of the computational cost and allows our mapping system to perform online. We evaluate the system on naturalistic road scenes, e.g., KITTI and observe a significant speed-up in the inference stage by labeling on the mesh.

scholarly journals Conflict Generalisation in ASP: Learning Correct and Effective Non-Ground Constraints

2020 ◽  
Vol 20 (5) ◽  
pp. 799-814
Author(s):  
RICHARD TAUPE ◽  
ANTONIUS WEINZIERL ◽  
GERHARD FRIEDRICH
Keyword(s):  
Computational Cost ◽  
Problem Instance ◽  
Test Cases ◽  
New Approach ◽  
Deductive Logic ◽  
Significant Performance ◽  
Speed Up ◽  
Problem Instances ◽  
Low Computational Cost ◽  
Additional Constraints

AbstractGeneralising and re-using knowledge learned while solving one problem instance has been neglected by state-of-the-art answer set solvers. We suggest a new approach that generalises learned nogoods for re-use to speed-up the solving of future problem instances. Our solution combines well-known ASP solving techniques with deductive logic-based machine learning. Solving performance can be improved by adding learned non-ground constraints to the original program. We demonstrate the effects of our method by means of realistic examples, showing that our approach requires low computational cost to learn constraints that yield significant performance benefits in our test cases. These benefits can be seen with ground-and-solve systems as well as lazy-grounding systems. However, ground-and-solve systems suffer from additional grounding overheads, induced by the additional constraints in some cases. By means of conflict minimization, non-minimal learned constraints can be reduced. This can result in significant reductions of grounding and solving efforts, as our experiments show.

scholarly journals UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 67-70
Author(s):  
X. G. Li ◽  
C. Ren ◽  
T. X. Zhang ◽  
Z. L. Zhu ◽  
Z. G. Zhang
Keyword(s):  
Image Matching ◽  
Consensus Algorithm ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Invariant Feature ◽  
Aerial Vehicle ◽  
Random Sample Consensus ◽  
Uav Image ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimental results show that compared with the SURF algorithm, SIFT (Scale Invariant Feature Transform) algorithm and ORB (Oriented FAST and Rotated BRIEF) algorithm, the proposed algorithm is superior to other algorithms in terms of matching accuracy and matching speed, and the robustness is higher.

scholarly journals Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1166
Author(s):  
Wei Zhang ◽  
Liang Gong ◽  
Suyue Chen ◽  
Wenjie Wang ◽  
Zhonghua Miao ◽  
...  
Keyword(s):  
Field Experiments ◽  
Absolute Error ◽  
Identification Accuracy ◽  
Depth Image ◽  
Dynamic Identification ◽  
Random Sample Consensus ◽  
Harvesting Efficiency ◽  
Value Decomposition ◽  
Forage Harvester ◽  
Ransac Algorithm

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

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