High accuracy 2D sub-pixel matching method skillfully managing error characteristics

2007 ◽  
Author(s):  
Hitoshi Nishiguchi ◽  
Yoshihiko Nomura ◽  
Ryota Sakamoto ◽  
Tokuhiro Sugiura
Keyword(s):  
High Accuracy ◽  
Matching Method ◽  
Pixel Matching ◽  
Error Characteristics

scholarly journals Extraction and Research of Crop Feature Points Based on Computer Vision

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2553 ◽  
Author(s):  
Jingwen Cui ◽  
Jianping Zhang ◽  
Guiling Sun ◽  
Bowen Zheng
Keyword(s):  
Computer Vision ◽  
Point Cloud ◽  
High Accuracy ◽  
Depth Camera ◽  
Positioning Error ◽  
Feature Points ◽  
Matching Method ◽  
Depth Images ◽  
Computer Vision Technology ◽  
Rgb Images

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

Feature Matching Based on Triangle Guidance and Constraints

2018 ◽  
Vol 32 (08) ◽  
pp. 1855014 ◽  
Author(s):  
Hongmin Liu ◽  
Hongya Zhang ◽  
Zhiheng Wang ◽  
Yiming Zheng
Keyword(s):  
Feature Matching ◽  
High Accuracy ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Matching Method ◽  
Shared Feature ◽  
Triangular Structure ◽  
Circle Center ◽  
Image Transformations

For images with distortions or repetitive patterns, the existing matching methods usually work well just on one of the two kinds of images. In this paper, we present novel triangle guidance and constraints (TGC)-based feature matching method, which can achieve good results on both kinds of images. We first extract stable matched feature points and combine these points into triangles as the initial matched triangles, and triangles combined by feature points are as the candidates to be matched. Then, triangle guidance based on the connection relationship via the shared feature point between the matched triangles and the candidates is defined to find the potential matching triangles. Triangle constraints, specially the location of a vertex relative to the inscribed circle center of the triangle, the scale represented by the ratio of corresponding side lengths of two matching triangles and the included angles between the sides of two triangles with connection relationship, are subsequently used to verify the potential matches and obtain the correct ones. Comparative experiments show that the proposed TGC can increase the number of the matched points with high accuracy under various image transformations, especially more effective on images with distortions or repetitive patterns due to the fact that the triangular structure are not only stable to image transformations but also provides more geometric constraints.

Pixel matching method in on-line three-dimensional measurement based on normalized equiphase plane

2017 ◽  
Vol 46 (7) ◽  
pp. 717004
Author(s):  
许幸芬 Xu Xingfen ◽  
曹益平 Cao Yiping ◽  
付光凯 Fu Guangkai ◽  
陈 澄 Chen Cheng ◽  
王亚品 Wang Yapin
Keyword(s):  
Three Dimensional ◽  
Matching Method ◽  
Dimensional Measurement ◽  
Pixel Matching ◽  
Three Dimensional Measurement ◽  
On Line

High-Accuracy Modeling of Ship Deformation Based on Inertial Measuring Method

2013 ◽  
Vol 760-762 ◽  
pp. 1227-1232 ◽  
Author(s):  
Er Kai Yuan ◽  
Gong Liu Yang
Keyword(s):  
Mathematical Models ◽  
Measurement Data ◽  
Measuring Method ◽  
High Accuracy ◽  
Analysis Method ◽  
Matching Method ◽  
Precision Estimation ◽  
Set Up ◽  
Better Than ◽  
Initial Measurement

High-accuracy modeling is the key problem of ship deformation measurement based on inertial measuring method. This paper is to find a high-accuracy modeling method based on the ship deformation data measured by optical devices. According to different modeling methods, mathematical models are designed by the analysis of the initial measurement data. Wavelet analysis method is introduced in the process of modeling. Attitude matching method is selected as the simulation algorithm and Kalman filter equations are set up based on the algorithm. Simulation results show that the ship deformation can be represented accurately by mathematical models. The Precision Estimation result is better than 0.4''.

Investigations into the Accuracy of the NRC Monocomparator

1966 ◽  
Vol 20 (4) ◽  
pp. 300-313
Author(s):  
V. Krishnamurty* ◽  
A. J. Smialowski
Keyword(s):  
High Accuracy ◽  
Individual Factors ◽  
Major Error ◽  
Error Characteristics ◽  
Overall Performance ◽  
The Individual ◽  
Very High

The NRC monocomparator is described briefly, followed by a detailed discussion of the individual factors that affect comparator measurements. The major error characteristics of the comparator are evaluated from measurements made on a precise glass grid and the resulting corrections are applied to all subsequent measurements. The paper concludes by presenting the results of the tests carried out to assess the overall performance of the NRC monocomparator. It has been proven that very high accuracy can be achieved by a simple and inexpensive instrument of the NRC monocomparator type.

A new pixel matching method using the entire modulation of the measured object in online PMP

Optik ◽  
2014 ◽  
Vol 125 (1) ◽  
pp. 137-140 ◽  
Author(s):  
Kuang Peng ◽  
Yi-ping Cao ◽  
Kun Li ◽  
Ying-chun Wu
Keyword(s):  
Matching Method ◽  
Measured Object ◽  
Pixel Matching

scholarly journals Comprehensive Pineapple Segmentation Techniques with Intelligent Convolutional Neural Network

2018 ◽  
Vol 10 (3) ◽  
pp. 1098
Author(s):  
Muhammad Azmi Ahmed Nawawi ◽  
Fatimah Sham Ismail ◽  
Hazlina Selamat
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Decision Maker ◽  
Conventional Method ◽  
Template Matching ◽  
High Accuracy ◽  
Matching Method ◽  
Pineapple Fruit ◽  
Segmentation Image ◽  
Normalized Difference Index

<span>This paper proposes an intelligent segmentation technique for pineapple fruit using Convolutional Neural Network (CNN). Cascade Object Detector (COD) method is used to detect the position of the pineapple from the captured image by returning the bounding box around the detecting pineapple. Image background such as ground, sky and other unwanted objects have been removed using Hue value, Adaptive Red and Blue Chromatic Map (ARB) and Normalized Difference Index (NDI) methods. However, the ARB and NDI methods are still producing misclassified error and the edge is not really smooth. In this case Template Matching Method (TMM) has been implemented for image enhancement process. Finally, an intelligent CNN is developed as a decision maker to select the best segmentation image ouput from ARB and NDI. The results obtained show that the proposed intelligent method has successfully verified the fruit from the background with high accuracy as compared to the conventional method.</span>

A fast pixel matching method based on phase feature extraction in online phase-measuring profilometry

2017 ◽  
Vol 64 (18) ◽  
pp. 1907-1914 ◽  
Author(s):  
Xingfen Xu ◽  
Yiping Cao ◽  
Yapin Wang ◽  
Cheng Chen ◽  
Guangkai Fu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Matching Method ◽  
Phase Measuring Profilometry ◽  
Pixel Matching

scholarly journals Sub-pixel matching method for low-resolution thermal stereo images

2020 ◽  
Vol 105 ◽  
pp. 103161
Author(s):  
Yannick Wend Kuni Zoetgnande ◽  
Geoffroy Cormier ◽  
Alain-Jérôme Fougéres ◽  
Jean-Louis Dillenseger
Keyword(s):  
Stereo Images ◽  
Low Resolution ◽  
Matching Method ◽  
Pixel Matching
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