A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

A technique of identifying the contour point to assess the change of abnormal areas on medical imaging

Assessing the growth or reduction of abnormal areas in medical imaging in general and tomography in particular is a greatly concerned and important research issue in recent years in Vietnam. The article presents a technique of identifying contour points of an abnormal area as a basis for later assessment of the development of abnormal area on medical imaging. The technique is based on the analysis of local structures in the vicinity of abnormal area boundaries combined with the use of convolutional neural networks and has been tested and evaluated based on 3D-IRCADb-01 sample data set with liver tumors.

An Improved ICCP Matching Algorithm for use in an Interference Environment during Geomagnetic Navigation

The Iterated Closest Contour Point (ICCP) algorithm is widely used in geomagnetic navigation. In order to enhance the anti-interference performance of the ICCP, an improved algorithm is proposed. First, the principle of delta modulation is introduced to generate a geomagnetic matching sequence according to the magnetic fluctuations, this assists finding the optimal quantitative step and matching length; thus, the algorithm's accuracy and real-time performance are improved. Second, in order to solve the problem of geomagnetic matching under an interference environment, a Probability Data Association (PDA) algorithm based on regenerated measurements is adopted. The ideal magnetic value is regarded as a target, and the measured values within the confidence region are taken as the effective measurements of the target. Each of them will give an estimation of the vehicle's position. Considering the constraints of a vehicle's kinematic performance, its final position can be obtained by fusing all effective estimations with the PDA algorithm. Simulation and semi-physical experiments have verified the feasibility and effectiveness of the proposed algorithm. The Regenerated Measurements (RM)-PDA algorithm shows better performance and can be used in practical applications.

Improved ICCP Algorithm Considering Scale Error for Underwater Geomagnetic Aided Inertial Navigation

The iterative closest contour point (ICCP) matching algorithm has become more and more widely used in the underwater geomagnetic aided inertial navigation system (INS). In practical application, the traditional ICCP algorithm is sensitive to the initial positioning error of the INS and can only do rigid transformation for the INS track of the vehicle. Particularly when there exists scale error, the accuracy and stability of the traditional ICCP algorithm will be affected. To solve this problem, an improved algorithm based on affine transformation is proposed. Firstly, the fundamental of the ICCP is analyzed in detail, and an error analysis of the geomagnetic aided inertial navigation system is carried out, and then the rigid transformation is replaced with affine transformation to improve the performance of the ICCP. In contrast to the conventional approach, the proposed algorithm can solve the rotation, translation, and scaling parameters of the indicated track and the matching track, so it can significantly reduce the interference of the scale error. Experimental results confirm the effectiveness of the proposed algorithm.

Contour-Point Signature Shape Descriptor for Point Correspondence

This paper introduces a novel descriptor technique denoted as Contour-Point Signature (CPS) useful to find correspondences of points selected from the outer contours of two arbitrary shapes, and to establish a relationship to map an ordered sequence of contour points from one shape to another. The proposal has proved to be invariant, to translation, scaling and rotation, it also induces a measure which is proved to be non-negative, unique, symmetric and identity-preserving. Experimental tests were performed in shape detection under noise, with image retrieval from a MPEG-7 database and letter recognition. Numerical results show that the proposal is robust for noise perturbation, as well as, having adequate accuracy and hit rate, even with coarse tuning for its parameters. This makes the method attractive to a wide range of applications.

An Adaptive △M-ICCP Geomagnetic Matching Algorithm

In this paper, a novel method is proposed to generate the matching sequence of an ICCP algorithm for aircraft geomagnetic-aided navigation based on the M coding principle. The length of the matching sequence and the selection of the matching points directly affects the performance of the Iterated Closest Contour Point (ICCP) algorithm. This study proposes an adaptive geomagnetic matching method, ΔM-ICCP, to solve the problem of selecting suitable matching lengths, and matching points, when a vehicle is moving in a highly dynamic environment. First, the △M coding principle is adopted to select the matching points based on the information of the magnetic field, the resolution of the magnetic map, and the accuracy of the magnetic sensor. Then, the problem of selecting parameters for the △M-ICCP algorithm is turned into an optimisation problem, which can be solved by a Binary Particle Swarm Optimisation (BPSO) algorithm. Finally, the algorithm is verified through simulation experiments. The proposed algorithm can provide a basis to determine the matching length of the △M-ICCP algorithm and adaptively adjust the algorithm's parameters according to different trajectories. The algorithm is applicable even in the areas where the fluctuations of Earth's magnetic field are not significant.