Empirical investigation of optimal window size using the least squares image matching method

1987 ◽  
Vol 42 (3) ◽  
pp. 113-125 ◽  
Author(s):  
Dan Rosenholm
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Empirical Investigation ◽  
Window Size ◽  
Matching Method ◽  
Optimal Window

scholarly journals OPTIMIZATION ALGORITHM OF TILTED IMAGE MATCHING BASED ON ADAPTIVE INITIAL OBJECT ASPECT

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 547-554
Author(s):  
C. Zhang ◽  
Y. Ge ◽  
Q. Zhang ◽  
B. Guo
Keyword(s):  
Least Squares ◽  
Image Matching ◽  
Normal Vector ◽  
Matching Method ◽  
Initial Value ◽  
Initial State ◽  
Matching Process ◽  
Low Degree ◽  
Verification Process ◽  
Initial Object

Abstract. When adopting the matching method of the least squares image based on object-patch to match tilted images, problems like the low degree of connection points for images with the discontinuity of depth or the discrepancy in elevation or low availability of aerotriangulation points would frequently appear. To address such problems, a tilted-image-matching algorithm based on an adaptive initial object-patch is proposed by this paper. By means of the existing initial values of the interior and exterior orientation elements of the tilted image and the information of object points generated in the matching process, the algorithm takes advantage of the method of multi-patch forward intersection and object variance partition so as to adaptively calculate the elevation of the object-patch and the initial value of the normal vector direction angle. Furthermore, this algorithm aims to solve the problem of difficulties in matching the tilted image with its corresponding points brought about by the low accuracy of the initial value of the tilted image when adopting the matching method of the least squares image based on object-patch to match the tilted image with high discrepancy in elevation. We adopt the algorithm as proposed in this paper and the least squares image matching method in which the initial state of the object-patch is horizontal to the object-patch respectively to conduct the verification process of comparing and matching two groups of tilted images. Finally, the effectiveness of the algorithm as proposed in this paper is verified by the testing results.

scholarly journals Particle Swarm Image Matching on Epipolar Line

2014 ◽  
Vol XL-4 ◽  
pp. 87-91
Author(s):  
J.-S. Hsia
Keyword(s):  
Image Matching ◽  
Particle Swarm ◽  
Window Size ◽  
Pso Algorithm ◽  
Least Square ◽  
Image Point ◽  
Reference Image ◽  
Epipolar Line ◽  
Matching Method ◽  
The Difference

This paper presents a method for determining the 3D position of an image point on a reference image using particle swarm optimization (PSO) to search the height (Z value) that gives the biggest Normalized Cross Correlation (NCC) coefficient. The searching area is in the surrounding of the height of the image point. The NCC coefficient evaluates the similarity with the image point and a corresponding point on an epipolar line in the search image. The position of corresponding image point on the epipolar line is determined by the height point on a sloping line locus. The PSO algorithm starts with a swarm of random particles. The position of each particle is a potential solution in the problem space. Each particle is given a randomized velocity and attracted toward the location of the best fitness. The position of each particle is iteratively modified by adding a newly computed velocity to its current position. The velocity is updated by three factors which are two attractions from local best position and global best position, two strengths of the attractions, and two uniform random numbers for each attraction. The iteration will stop when the current solution is convergent. The time of computation is highly related to the range of height and the interval of height enumeration when the approach to find a corresponding image point of an image point on a reference image is based on the height enumeration along sloping line locus. The precision of results can be improved by decreasing the interval of height enumeration. This shows the limitation of the enumeration method in the efficiency and accuracy. The issue is overcome by a method of using PSO algorithm. The proposed method using different parameters such as the size of image window, the number of particles, and the size of the height searching range has been applied to aerial stereo images. The accuracy of tested results is evaluated on the base of the comparison to the reference data from the results of least-square matching being manually given initial points. The evaluation result shows that tested results has given a solution to a level of less than 1 centimetre without using refined image matching method. The same level of accuracy can reach even when the searching range is bigger than 90 meters. But the difference of image window size may lead to the change of the matching result. And, without the procedures of both coarse-to-fine hierarchical solution and refined image matching method, the algorithm still can give the same accuracy level of least-square image matching resulting. This method also shows its ability to give reasonable matching results without manual assistance.

scholarly journals A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

2021 ◽  
Vol 13 (8) ◽  
pp. 1442
Author(s):  
Kaisen Ma ◽  
Yujiu Xiong ◽  
Fugen Jiang ◽  
Song Chen ◽  
Hua Sun
Keyword(s):  
Point Cloud ◽  
Local Maximum ◽  
Window Size ◽  
Point Clouds ◽  
High Density ◽  
Watershed Algorithm ◽  
Canopy Area ◽  
Individual Trees ◽  
Optimal Window ◽  
Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

Identification of the Hook on Investment Casting Shell Line Based on Machine Vision

2012 ◽  
Vol 220-223 ◽  
pp. 1356-1361
Author(s):  
Xi Jie Tian ◽  
Jing Yu ◽  
Chang Chun Li
Keyword(s):  
Machine Vision ◽  
Investment Casting ◽  
Hough Transform ◽  
Image Matching ◽  
Feature Matching ◽  
Image Acquisition ◽  
Target Range ◽  
Target Area ◽  
Matching Method ◽  
Vertical Projection

In this paper, the idea identify the hook on investment casting shell line based on machine vision has been proposed. According to the characteristic of the hook, we do the image acquisition and preprocessing, we adopt Hough transform to narrow the target range, and find the target area based on the method combining the level projection and vertical projection, use feature matching method SIFT to do the image matching. Finally, we get the space information of the target area of the hook.

Does Family Decision-Making Power Improve Women’s Happiness?

2021 ◽  
pp. 0192513X2110300
Author(s):  
Zhongwu Li
Keyword(s):  
Decision Making ◽  
Propensity Score ◽  
Least Squares ◽  
Logit Model ◽  
Self Esteem ◽  
Ordinary Least Squares ◽  
Matching Method ◽  
Family Decision ◽  
Family Decision Making ◽  
Heterogeneity Analysis

It is almost a consensus that the stronger family decision-making power a woman has, the happier she will be. While using the China Family Panel Studies, this study reveals a long-overlooked fact that women’s control over more family decision-making power does not necessarily improve their happiness. The results of the ordinary least squares and ordinal logit model confirm this finding, and the propensity score matching method corroborates the conclusion. Heterogeneity analysis shows that among those women with less education and lower social status, the negative happiness effect of women’s family decision-making power is particularly significant. Women’s traditional attitudes and self-esteem are two important factors which hinder women’s family decision-making power from enhancing their happiness.

Implementation of Verification and Matching E-KTP with Faster R-CNN and ORB

2021 ◽  
Vol 5 (4) ◽  
pp. 783-793
Author(s):  
Muhammad Muttabi Hudaya ◽  
Siti Saadah ◽  
Hendy Irawan
Keyword(s):  
Image Matching ◽  
Nearest Neighbor ◽  
Feature Matching ◽  
Image Feature ◽  
Brute Force ◽  
K Nearest Neighbor ◽  
Matching Method ◽  
Average Precision ◽  
Detection Model ◽  
Matching Process

needs a solid validation that has verification and matching uploaded images. To solve this problem, this paper implementing a detection model using Faster R-CNN and a matching method using ORB (Oriented FAST and Rotated BRIEF) and KNN-BFM (K-Nearest Neighbor Brute Force Matcher). The goal of the implementations is to reach both an 80% mark of accuracy and prove matching using ORB only can be a replaced OCR technique. The implementation accuracy results in the detection model reach mAP (Mean Average Precision) of 94%. But, the matching process only achieves an accuracy of 43,46%. The matching process using only image feature matching underperforms the previous OCR technique but improves processing time from 4510ms to 60m). Image matching accuracy has proven to increase by using a high-quality dan high quantity dataset, extracting features on the important area of EKTP card images.

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