scholarly journals Monocular Vision-Based Pose Determination in Close Proximity for Low Impact Docking

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3261 ◽  
Author(s):  
Liu ◽  
Xu ◽  
Zhu ◽  
Zhao
Keyword(s):  
Image Processing ◽  
Large Time ◽  
Monocular Vision ◽  
Point Problem ◽  
Unified Framework ◽  
Position Measurement ◽  
Pose Determination ◽  
Time Performance ◽  
Close Proximity ◽  
Docking Mechanism

Pose determination in close proximity is critical for space missions in which monocular vision is one of the most promising solutions. Although numerous approaches such as using artificial beacons or specific shapes on spacecrafts have proved to be effective, the high individuation and the large time delay limit their use in low impact docking. This paper proposes a unified framework to determinate the relative pose between two docking mechanisms by treating their guide petals as measurement objects. Fusing the pose information of one docking mechanism to simplify image processing and creating an intermediate coordinate system to solve the perspective-n-point problem greatly improve the real-time performance and the robustness of the method. Experimental results show that the position measurement error is within 3.7 mm, while the rotation error around docking direction is less than 0.16°, corresponding to a measurement time reduction of 85%.

scholarly journals Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Zhou ◽  
Chengdong Wu ◽  
Dali Chen ◽  
Zhenzhu Wang ◽  
Yugen Yi ◽  
...  
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Dictionary Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Critical Role ◽  
Medical Image Processing ◽  
Unified Framework ◽  
Semantic Relationships ◽  
Detection Approach

Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

A review of cooperative and uncooperative spacecraft pose determination techniques for close-proximity operations

2017 ◽  
Vol 93 ◽  
pp. 53-72 ◽  
Author(s):  
Roberto Opromolla ◽  
Giancarmine Fasano ◽  
Giancarlo Rufino ◽  
Michele Grassi
Keyword(s):  
Proximity Operations ◽  
Pose Determination ◽  
Close Proximity

scholarly journals Flight Performance Analysis of an Image Processing Algorithm for Integrated Sense-and-Avoid Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Lidia Forlenza ◽  
Giancarmine Fasano ◽  
Domenico Accardo ◽  
Antonio Moccia
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Data Rate ◽  
Obstacle Detection ◽  
Image Processing Technique ◽  
Identification System ◽  
Flight Performance ◽  
Image Processing Algorithm ◽  
Position Measurement ◽  
Detection Range

This paper is focused on the development and the flight performance analysis of an image-processing technique aimed at detecting flying obstacles in airborne panchromatic images. It was developed within the framework of a research project which aims at realizing a prototypical obstacle detection and identification System, characterized by a hierarchical multisensor configuration. This configuration comprises a radar, that is, the main sensor, and four electro-optical cameras. Cameras are used as auxiliary sensors to the radar, in order to increase intruder aircraft position measurement, in terms of accuracy and data rate. The paper thoroughly describes the selection and customization of the developed image-processing techniques in order to guarantee the best results in terms of detection range, missed detection rate, and false-alarm rate. Performance is evaluated on the basis of a large amount of images gathered during flight tests with an intruder aircraft. The improvement in terms of accuracy and data rate, compared with radar-only tracking, is quantitatively demonstrated.

A Dynamic Position Measurement Method for Destructive Process of Structures Using Image Processing

2004 ◽  
Author(s):  
Hiroshi Nishizawa ◽  
Satoshi Fujita ◽  
Osamu Furuya
Keyword(s):  
Image Processing ◽  
High Precision ◽  
High Speed ◽  
Spherical Surface ◽  
Three Dimensional ◽  
Measuring System ◽  
Position Measurement ◽  
Surface Emission ◽  
Optical Motion ◽  
Optical Motion Capture

In order to clarify the destruction mechanism of large structures in large seismic movements, a non-contacting displacement measurement system with a three-dimensional dynamic position with high precision is required. We have developed a three-dimensional measuring system with image processing using optical motion capture technology. This system consists of light emitting markers installed on the object structure and plural high speed cameras which obtain images of markers’ movement simultaneously, to measure the dynamic position of the three dimensional spatial coordinates of the markers. In order to measure the dynamic position with high precision, we have ever developed sub-pixel processing method which is able to measure very small displacements of the markers by analyzing the luminance distribution. Moreover, we have developed a new marker of spherical surface emission type which formed the luminance profile to improve furthermore the accuracy in rotational movement. Shaking tests were carried out with this measuring system and the results indicated that this system using new markers had sufficient accuracy within errors of a few millimeters in the structure of a 4 meter cube. Consequently, we have acquired the potential to apply to the measurement to the 3-D Full Scale Earthquake Testing Facility (E-Defense).

scholarly journals Subpixel Localization of Isolated Edges and Streaks in Digital Images

2020 ◽  
Vol 6 (5) ◽  
pp. 33
Author(s):  
Devin T. Renshaw ◽  
John A. Christian
Keyword(s):  
Image Processing ◽  
Iterative Methods ◽  
Digital Images ◽  
State Of The Art ◽  
Measurement Data ◽  
Unified Framework ◽  
Signal To Noise ◽  
Sensing Systems ◽  
Current State ◽  
Common Signal

Many modern sensing systems rely on the accurate extraction of measurement data from digital images. The localization of edges and streaks in digital images is an important example of this type of measurement, with these techniques appearing in many image processing pipelines. Several approaches attempt to solve this problem at both the pixel level and subpixel level. While the subpixel methods are often necessary for applications requiring best-possible accuracy, they are often susceptible to noise, use iterative methods, or require pre-processing. This work investigates a unified framework for subpixel edge and streak localization using Zernike moments with ramp-based and wedge-based signal models. The method described here is found to outperform the current state-of-the-art for digital images with common signal-to-noise ratios. Performance is demonstrated on both synthetic and real images.

scholarly journals An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1353 ◽  
Author(s):  
Wugang Zhang ◽  
Wei Guo ◽  
Chuanwei Zhang ◽  
Shuanfeng Zhao
Keyword(s):  
Real Time ◽  
Calibration Method ◽  
System Structure ◽  
Data Sets ◽  
Position Measurement ◽  
Online Calibration ◽  
Time Performance ◽  
Calibration Methods ◽  
Testing Data ◽  
Mean Square Errors

The online calibration method of a two-dimensional (2D) galvanometer requires both high precision and better real-time performance to meet the needs of moving target position measurement, which presents some challenges for traditional calibration methods. In this paper, a new online calibration method is proposed using the wavelet kernel extreme learning machine (KELM). Firstly, a system structure is created and its experiment setup is established. The online calibration method is then analyzed based on a wavelet KELM algorithm. Finally, the acquisition methods of the training data are set, two groups of testing data sets are presented, and the verification method is described. The calibration effects of the existing methods and wavelet KELM methods are compared in terms of both accuracy and speed. The results show that, for the two testing data sets, the root mean square errors (RMSE) of the Mexican Hat wavelet KELM are reduced by 16.4% and 38.6%, respectively, which are smaller than that of the original ELM, and the standard deviations (Sd) are reduced by 19.2% and 36.6%, respectively, indicating the proposed method has better generalization and noise suppression performance for the nonlinear samples of the 2D galvanometer. Although the online operation time of KELM is longer than ELM, due to the complexity of the wavelet kernel, it still has better real-time performance.

Image Analysis

2013 ◽  
pp. 1-14
Author(s):  
Detlev Droege
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Eye Tracking ◽  
Real Time ◽  
Basic Knowledge ◽  
Eye Tracker ◽  
Tracking Systems ◽  
Program Code ◽  
Time Performance

This chapter focuses on the image processing part of eye tracking systems. Basic knowledge of image processing is assumed. After an overview of the possible input images and some remarks on preprocessing of the images, we will focus on the detection relevant features such as pupils and glints. The last part of this chapter focuses on estimating positions of these features. It is not possible to present a comprehensive solution for an eye tracker in this chapter; however, we will indicate possible yet simplified methods in the different steps of processing and demonstrate how images can be processed to obtain real-time performance. The program code is given in Matlab (Octave) language for clarity.

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