scholarly journals Rail Fastener Positioning Based on Double Template Matching

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yijin Qiu ◽  
Xingjie Chen ◽  
Zhaomin Lv
Keyword(s):  
Template Matching ◽  
Two Dimensions ◽  
Original Image ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
Horizontal Dimension ◽  
Positioning Strategy ◽  
Template Image ◽  
Positioning Method ◽  
Coarse To Fine

For global template matching (GTM), which is commonly used in the positioning of rail fasteners, only the fastener template is used to search the global image in both two dimensions, which will result in errors in two dimensions, and the lower positioning accuracy will be caused. A positioning method for rail fasteners based on double template matching (DTM) is proposed in this paper, in which the double template contains the rail template and the fastener template. First, the rail template is used to scan the original image in horizontal dimension, and the squared Euclidean distance (SED) is used to obtain the rail positioning in the original image. Combining with the prior knowledge of the fastener template image, the image composed of the rail and the fastener can be obtained, which is called the Rail Area Map (RAM) in this paper. Then, after preprocessing the RAM and the fastener template image, the fastener template image is used to scan the RAM in vertical dimension, and the normalized correlation coefficient (NCC) is used to calculate the similarity between the template and the subgraph of the RAM to achieve precise positioning of the fastener. The proposed DTM method adopts a positioning strategy from coarse to fine, and two templates are used to complete different positioning tasks in their own dimension, respectively. Due to the rail can be precise positioned in horizontal dimension, the error of the fastener positioning in the horizontal dimension can be avoided, and thus, the positioning accuracy can be improved. Experiments on the on-site line fastener images prove that the proposed method can effectively achieve the precise positioning of fasteners.

scholarly journals Research on track fastener positioning method based on local unidirectional template matching

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Liang Li ◽  
Zhaomin Lv ◽  
Xingjie Chen ◽  
Yijin Qiu ◽  
Liming Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Template Matching ◽  
Surrounding Area ◽  
Vertical Projection ◽  
Ballastless Track ◽  
Complex Background ◽  
Template Image ◽  
Ballasted Track ◽  
Positioning Method ◽  
Segmentation Threshold

Commonly used fastener positioning methods include pixel statistics (PS) method and template matching (TM) method. For the PS method, it is difficult to judge the image segmentation threshold due to the complex background of the track. For the TM method, the search in both directions of the global is easily affected by complex background, as a result, the locating accuracy of fasteners is low. To solve the above problems, this paper combines the PS method with the TM method and proposes a new fastener positioning method called local unidirectional template matching (LUTM). First, the rail positioning is achieved by the PS method based on the gray-scale vertical projection. Then, based on the prior knowledge, the image of the rail and the surrounding area of the rail is obtained which is referred to as the 1-shaped rail image; then, the 1-shaped rail image and the produced offline symmetrical fastener template is pre-processed. Finally, the symmetrical fastener template image is searched from top to bottom along the rail and the correlation is calculated to realize the fastener positioning. Experiments have proved that the method in this paper can effectively realize the accurate locating of the fastener for ballastless track and ballasted track at the same time.

Experimental Vision-Based Target Positioning by PI Micro-Robot

2013 ◽  
Vol 433-435 ◽  
pp. 760-765 ◽  
Author(s):  
Wei Song ◽  
Xu Liu ◽  
Ya Nan Zhang ◽  
Lin Yong Shen
Keyword(s):  
Template Matching ◽  
Physical Experiment ◽  
Positioning Accuracy ◽  
Spatial Coordinate ◽  
Physical Experiments ◽  
Micro Robot ◽  
Confinement Fusion ◽  
Positioning Method ◽  
Feature Values ◽  
Positioning Algorithm

In Inertia confinement fusion (ICF) physical experiments, target positioning accuracy directly affects the success of target hitting. The proposed positioning method firstly used template matching to extract the target features in image, then calculated the target’s spatial coordinate and rotation matrix by integrating the feature values from three CCDs. We used a PI Hexapods Micro-robot to adjust the target to a desired position. The experiment results show the PI Hexapods Micro-robot is confirmed to be able to adjust the target in a desired position, which verifies the practicality of the proposed positioning algorithm to be used in the real ICF physical experiment.

scholarly journals Repeat pattern segmentation of print fabric based on adaptive template matching

2020 ◽  
Vol 15 ◽  
pp. 155892502097328
Author(s):  
Zhong Xiang ◽  
Ding Zhou ◽  
Miao Qian ◽  
Miao Ma ◽  
Yang Liu ◽  
...  
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Edge Density ◽  
Original Image ◽  
Edge Structure ◽  
Template Image ◽  
Pattern Retrieval ◽  
Pattern Segmentation ◽  
Periodic Repetition ◽  
Repeat Pattern

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

scholarly journals Precise Linear Positioning Method Based on Transmissive Two-Stage Diffraction Grating System

2021 ◽  
Vol 2083 (2) ◽  
pp. 022037
Author(s):  
Jin Lu ◽  
Jiannan Lu ◽  
Jun Ma ◽  
Zaiyan Gong
Keyword(s):  
Diffraction Grating ◽  
Simulation Experiment ◽  
Two Stage ◽  
Positioning Accuracy ◽  
Precise Positioning ◽  
Differential Positioning ◽  
Positioning Method ◽  
Positioning Device ◽  
Grating Diffraction ◽  
The Mathematical Model

Abstract This paper mainly studies the precise linear positioning method based on the transmissive type two-stage diffraction grating system. Starting from the analysis of the two-stage diffraction principle, the mathematical model of the two-stage grating diffraction is established, and the positioning characteristics of the differential positioning method and the modified positioning method are discussed. The simulation experiment of the linear positioning device is carried out to study the displacement characteristics. The experimental results show that the precise positioning based on the diffraction grating can obtain a positioning accuracy of ±0.4 μm.

Precise positioning method for multi-process connecting based on binocular vision

2016 ◽  
Author(s):  
Wei Liu ◽  
Lichao Ding ◽  
Kai Zhao ◽  
Xiao Li ◽  
Ling Wang ◽  
...  
Keyword(s):  
Binocular Vision ◽  
Precise Positioning ◽  
Positioning Method

scholarly journals Indoor Positioning Method Based on Infrared Vision and UWB Fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012070
Author(s):  
Qianrong Zhang ◽  
Yi Li
Keyword(s):  
Real Time ◽  
Indoor Localization ◽  
Low Cost ◽  
Ultra Wideband ◽  
Coverage Area ◽  
Positioning Accuracy ◽  
Mobile Vehicle ◽  
Positioning Method ◽  
Positioning Algorithm ◽  
Infrared Vision

Abstract Ultra-wideband (UWB) has broad application prospects in the field of indoor localization. In order to make up for the shortcomings of ultra-wideband that is easily affected by the environment, a positioning method based on the fusion of infrared vision and ultra-wideband is proposed. Infrared vision assists locating by identifying artificial landmarks attached to the ceiling. UWB uses an adaptive weight positioning algorithm to improve the positioning accuracy of the edge of the UWB positioning coverage area. Extended Kalman filter (EKF) is used to fuse the real-time location information of the two. Finally, the intelligent mobile vehicle-mounted platform is used to collect infrared images and UWB ranging information in the indoor environment to verify the fusion method. Experimental results show that the fusion positioning method is better than any positioning method, has the advantages of low cost, real-time performance, and robustness, and can achieve centimeter-level positioning accuracy.

scholarly journals Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongbin Pan ◽  
Yang Xiang ◽  
Jian Xiong ◽  
Yifan Zhao ◽  
Ziwei Huang ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Detection Method ◽  
Access Point ◽  
Combination Method ◽  
Data Set ◽  
Positioning Accuracy ◽  
Multipath Effects ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Mismatch Rate

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

scholarly journals An Improved Hierarchical WLAN Positioning Method Based on Apriori Knowledge

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 475 ◽  
Author(s):  
Kwo-Ting Fang ◽  
Cheng-Tao Lee ◽  
Li-min Sun
Keyword(s):  
Compressive Sensing ◽  
Wireless Local Area Network ◽  
Local Area Network ◽  
Signal Reconstruction ◽  
Local Area ◽  
Area Network ◽  
Fingerprint Matching ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Fine Localization

The hierarchical-based structure is recognized as a favorable structure for wireless local area network (WLAN) positioning. It is comprised of two positioning phases: the coarse localization phase and the fine localization phase. In the coarse localization phase, the users’ positions are firstly narrowed down to smaller regions or clusters. Then, a fingerprint matching algorithm is adopted to estimate the users’ positions within the clusters during the fine localization phase. Currently the clustering strategies in the coarse localization phase can be divided into received signal strength (RSS) clustering and 3D clustering. And the commonly seen positioning algorithms in the fine localization phase include k nearest neighbors (kNN), kernel based and compressive sensing-based. This paper proposed an improved WLAN positioning method using the combination: 3D clustering for the coarse localization phase and the compressive sensing-based fine localization. The method have three favorable features: (1) By using the previously estimated positions to define the sub-reference fingerprinting map (RFM) in the first coarse localization phase, the method can adopt the prior information that the users’ positions are continuous during walking to improve positioning accuracy. (2) The compressive sensing theory is adopted in the fine localization phase, where the positioning problem is transformed to a signal reconstruction problem. This again can improve the positioning accuracy. (3) The second coarse localization phase is added to avoid the global fingerprint matching in traditional 3D clustering-based methods when the stuck-in-small-area problem is encountered, thus, sufficiently lowered the maximum positioning latency. The proposed improved hierarchical WLAN positioning method is compared with two traditional methods during the experiments section. The resulting positioning accuracy and positioning latency have shown that the performance of the proposed method has better performance in both aspects.

scholarly journals Mobile Robot Indoor Positioning System Based on K-ELM

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Haixia Wang ◽  
Junliang Li ◽  
Wei Cui ◽  
Xiao Lu ◽  
Zhiguo Zhang ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Positioning Algorithm ◽  
Indoor Positioning System

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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