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A novel vision-based relative navigation system (VBRNS) plays an important role in aeronautics and astronautics fields, and the filter is the core of VBRNS. However, most of the existing filtering algorithms used in VBRNS are derived based on Gaussian assumption and disregard the non-Gaussianity of VBRNS. Therefore, a novel robust filtering named as cubature Huber-based filtering (CHF) is proposed and applied to VBRNS to improve the navigation accuracy in non-Gaussian noise case.

Under the Bayesian filter framework, the third-degree cubature rule is used to compute the cubature points which are propagated through state equation, and then the predicted mean and the associated covariance are taken. A combined minimum l₁ and l₂-norm estimation method referred as Huber’s criterion is used to design the measurement update. After that, the vision-based relative navigation model is presented and the CHF is used to integrate the line-of-sight measurements from vision camera with inertial measurement of the follower to estimate the precise relative position, velocity and attitude between two unmanned aerial vehicles. During the design of relative navigation filter, the quaternions are used to represent the attitude and the generalized Rodrigues parameters are used to represent the attitude error. The simulation is conducted to demonstrate the effectiveness of the algorithm.

By this means, the VBRNS could perform better than traditional VBRNS whose filter is designed by Gaussian filtering algorithms. And the simulation results demonstrate that the CHF could exhibit robustness when the system is non-Gaussian. Moreover, the CHF has more accurate estimation and faster rate of convergence than extended Kalman Filtering (EKF) in face of inaccurate initial conditions.

A novel robust nonlinear filtering algorithm named as CHF is proposed and applied to VBRNS based on cubature Kalman filtering (CKF) and Huber’s technique. The CHF could adapt to the non-Gaussian system effectively and perform better than traditional Gaussian filtering such as EKF.

The purpose of this paper is to develop an effective and reliable corner detection algorithm so as to extract all the desired corners successfully. In addition, the influences of edge detection method as well as smoothing technique on the overall performance of corner detection techniques are investigated.

In this paper, an effective corner detection algorithm based on subpixel edge detector and Gaussian filter is presented. First, a subpixel accuracy edge detector is used rather than a pixel accuracy edge detector to detect edges. Second, B‐splines approximation technique is used to eliminate the staircase effect of a digital curve. Third, curvature curve derived from the edges is smoothed by a Gaussian filter. Finally, statistical process control technique is applied to detect vertices.

The results show that spatial‐moment outperforms chain code as an edge detector. Furthermore, the Gaussian filter should be used to smooth curvature curve instead of smoothing the profile of an object, because the former provides greater impact on the corner detection results.

In addition to object recognition, motion tracking and obstacle avoidance, the proposed method also has many important engineering and manufacturing applications such as dimensional measuring, reverse engineering, and machine vision‐based computer numerical control (CNC) machining of polygonal sheet metal parts.

This paper aims to propose Bayesian filtering based on solving the Fokker–Planck equation, to improve the accuracy of filtering in non-Gauss case. Nonlinear filtering plays an important role in many science and engineering fields for estimating the state of dynamic system, but the existing filtering algorithms are mainly used for solving the problem of Gauss system.

Under the Bayesian framework, the time update of this filtering is based on solving Fokker–Planck equation, while the measurement update uses the Bayes formula directly. Therefore, this novel algorithm can be applied to nonlinear, non-Gaussian estimation. To reduce the computational complexity due to standard meshing, an adaptive meshing algorithm proposed which includes the coarse meshing, significant domain determination that is generated using extended Kalman filtering and Chebyshev’s inequality theorem, and value assignment for significant domain. Simulations are conducted on a reentry body tracking problem to demonstrate the effectiveness of this novel algorithm.

In this way, finer grid points can be placed in the regions with high conditional probability density, while the grid points with low conditional probability density can be neglected. The simulation results indicate that the novel algorithm can reduce the computational burden significantly compared to the standard meshing, while achieving similar accuracy.

A novel Bayesian filtering based on solving the Fokker–Planck equation using adaptive meshing is proposed, and the simulations show that algorithm can reduce the computational burden significantly compared to the standard meshing, while achieving similar accuracy.

A novel nonlinear filtering based on solving the Fokker–Planck equation is proposed. The novel algorithm is suitable for non-Gauss system, and can achieve similar accuracy compared to the standard meshing with the significant reduction of computational burden.

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition classification. The estimated results for each class of images and each method have shown improved image quality for the six categories of document images described by their separate characteristics.

The applied technique consists of five stages, i.e. text image acquisition, image preparation, denoising, image type classification in six categories according to image condition, image thresholding and final refinement, a very effective approach to binarize document images. The results achieved by the authors' method require minimal pre‐processing steps for best quality of the image and increased text readability. This methodology performs better compared to current state‐of‐the‐art adaptive thresholding techniques.

An innovative procedure for digital historical documents image binarization based on image pre‐processing, image type classification in categories according to image condition and further enhancement. This methodology is robust and simple, with minimal pre‐processing steps for best quality of the image, increased text readability and it performs better compared to available thresholding techniques.

The technique consists of limited but optimized pre‐processing sequential steps, and attention should be given in document image preparation and denoising, and on image condition classification for thresholding and refinement, since bad results in a single stage corrupt the final document image quality and text readability.

The paper contributes in digital image binarization of text images suggesting a procedure based on image preparation, image type classification and thresholding and image refinement with applicability on Byzantine historical documents.

The purpose of this study is to establish a method for accurately extracting torch and seam features. This will improve the quality of narrow gap welding. An adaptive deflection correction system based on passive light vision sensors was designed using the Halcon software from MVtec Germany as a platform.

This paper proposes an adaptive correction system for welding guns and seams divided into image calibration and feature extraction. In the image calibration method, the field of view distortion because of the position of the camera is resolved using image calibration techniques. In the feature extraction method, clear features of the weld gun and weld seam are accurately extracted after processing using algorithms such as impact filtering, subpixel (XLD), Gaussian Laplacian and sense region for the weld gun and weld seam. The gun and weld seam centers are accurately fitted using least squares. After calculating the deviation values, the error values are monitored, and error correction is achieved by programmable logic controller (PLC) control. Finally, experimental verification and analysis of the tracking errors are carried out.

The results show that the system achieves great results in dealing with camera aberrations. Weld gun features can be effectively and accurately identified. The difference between a scratch and a weld is effectively distinguished. The system accurately detects the center features of the torch and weld and controls the correction error to within 0.3mm.

An adaptive correction system based on a passive light vision sensor is designed which corrects the field-of-view distortion caused by the camera’s position deviation. Differences in features between scratches and welds are distinguished, and image features are effectively extracted. The final system weld error is controlled to 0.3 mm.

The purpose of this paper is to make the test of flexible printed circuit (FPC) board be more efficient and accurate.

This paper aims to segment an image through applying the technology of computer pattern recognition.

The results indicate that the automatic detection system of FPC board’s defect has high efficiency and accuracy in accordance with the requirements of detection.

This paper managed image segmentation through applying the technology of computer pattern recognition.

Large amount of data are stored in image format. Image retrieval from bulk databases has become a hot research topic. An alternative method for efficient image retrieval is proposed based on a combination of texture and colour information. The main purpose of this paper is to propose a new content based image retrieval approach using combination of color and texture information in spatial and transform domains jointly.

Various methods are provided for image retrieval, which try to extract the image contents based on texture, colour and shape. The proposed image retrieval method extracts global and local texture and colour information in two spatial and frequency domains. In this way, image is filtered by Gaussian filter, then co-occurrence matrices are made in different directions and the statistical features are extracted. The purpose of this phase is to extract noise-resistant local textures. Then the quantised histogram is produced to extract global colour information in the spatial domain. Also, Gabor filter banks are used to extract local texture features in the frequency domain. After concatenating the extracted features and using the normalised Euclidean criterion, retrieval is performed.

The performance of the proposed method is evaluated based on the precision, recall and run time measures on the Simplicity database. It is compared with many efficient methods of this field. The comparison results showed that the proposed method provides higher precision than many existing methods.

The comparison results showed that the proposed method provides higher precision than many existing methods. Rotation invariant, scale invariant and low sensitivity to noise are some advantages of the proposed method. The run time of the proposed method is within the usual time frame of algorithms in this domain, which indicates that the proposed method can be used online.

Most of the passive techniques for enhancing heat transfer inside pipes (e.g. rough surfaces, swirl-flow devices and coiled tubes) give origin to an irregular distribution of the heat transfer coefficient at the fluid–wall interface along the wall perimeter. This irregular distribution could be critical in some industrial applications, but most of the available research papers, mainly due to the practical difficulty of local measuring heat flux on the internal wall surface of a pipe, present the results only in terms of Nusselt number averaged along the wall circumference. This paper aims to study the application of inverse problem solution techniques, which could overcome this limitation.

With regard to the estimation of the local convective heat transfer coefficient in coiled tubes, two different inverse heat conduction problem solution techniques were considered and compared both by synthetic and experimental data.

The paper shows the success of two inverse problem solution techniques in the estimation of the local convective heat transfer coefficient in coiled tubes.

This paper fulfills an identified need because most of the available research papers present the results only in terms of average thermal performance, neglecting local behavior.

Aiding information is frequently adopted to calibrate the errors from inertia-generated trajectories in pedestrian positioning. However, existing calibration methods lack interior connections and unanimity, making it difficult to incorporate multiple sources of aiding information. This paper aims to propose a unanimous anchor-based trajectory calibration framework, which is expandable to encompass different types of anchor information.

The concept of anchors is introduced to represent different types of aiding information, which are, in essence, different constraint conditions on inertia-derived raw trajectories. The foundation of the framework is a particle filter which is implemented based on various particle weight updating strategies using diverse types of anchor information. Herein, three representative anchors are chosen to elaborate and validate the proposed framework, namely, ultra-wide-band (UWB) ranging anchors, iBeacons and the building structure-based virtual anchors.

In the simulations, with the particle reweighting strategies of the proposed framework, the positioning errors can be compensated. In the experimental test in an office building in which three anchors, including one UWB anchor, one iBeacon and one building structure-based virtual anchor are deployed; the final positioning error is decreased from 1.9 to 1.2 m; and the heading error is reduced from about 21° to 7°, respectively.

Herein, an anchor-based unanimous trajectory calibration framework for inertial pedestrian positioning is proposed. This framework is applicable to the schemes with different configurations of the anchors and can be expanded to adopt as much anchor information as possible.

The purpose of this study was expected to simultaneously monitor the surface roughness of each solidified layer, the surface roughness of the metal powder, the outline of the solidified layer, and the height difference between the solidified layer and the metal powder.

In the proposed approach, color images with red, green and blue fringes are used to measure the shape of the built object using a three-step phase-shift algorithm and phase-unwrapping method. In addition, the surface roughness is extracted from the speckle information in the captured image using a predetermined autocorrelation function.

The feasibility and accuracy of the proposed system were validated by comparing it with a commercial system for an identical set of samples fabricated by a selective laser melting process. The maximum and minimum errors between the two systems are approximately 24% and 0.8%, respectively.

In the additive manufacturing field, the authors are the first to use fringe detection technology to simultaneously measure the profile of the printed layer and its surface roughness.