An Ellipse Fitting Algorithm Applied to the Construction of the Elliptic Arc-Shaped Leading Edge

2013 ◽  
Vol 744 ◽  
pp. 232-238
Author(s):  
Xiao Qiang Yu ◽  
Tao Ning ◽  
Yong Long Hou ◽  
Zhi Tong Chen
Keyword(s):  
Numerical Experiments ◽  
Leading Edge ◽  
Ellipse Fitting ◽  
Circle Fitting ◽  
Tangential Contact ◽  
Contact Constraint ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Strong Robustness ◽  
Precision And Accuracy

In this paper, we proposed a new ellipse fitting method converting the ellipse fitting into a circle fitting based on projection transformation. Numerical experiments demonstrate that our approach have good stability and strong robustness against noise in addition to excellent precision and accuracy. In our further research, we successfully extend our algorithm to the construction of the elliptic arc-shaped leading edge by using a tangential contact constraint. Keywords: Ellipse fitting; Projection; PSO; Leading edge

Research on Roadbed Settlement Measurement with Laser Spot Detection Based on Ellipse Fitting

2014 ◽  
Vol 530-531 ◽  
pp. 113-116
Author(s):  
Jing Hong Xing ◽  
Xiang Wei Li ◽  
Lang Tao Zhao
Keyword(s):  
Curve Fitting ◽  
Optical Axis ◽  
Laser Spot ◽  
Ellipse Fitting ◽  
Edge Extraction ◽  
Circle Fitting ◽  
Spot Center ◽  
Spot Detection ◽  
Fitting Algorithm ◽  
Canny Edge

Detecting the location offset of laser spot center method to monitor roadbed settlement is a kind of portable, easy to layout method. Detection laser spot center offset, we can use gravity, circle fitting, ellipse fitting and Gaussian curve fitting, etc method. Gravity algorithm is only involves basic arithmetic and low precise, and ellipse fitting is higher precise. Because of the angle between the optical axis and the target, the laser spot is more approximate as a ellipse. Using the ellipse fitting algorithm, Sub-pixel precise threshold segmentation and Canny edge extraction we obtained an ideal monitoring precise of the roadbed settlement.

AN IMPROVED RANDOM WALK SEGMENTATION ON THE LUNG NODULES

2013 ◽  
Vol 06 (06) ◽  
pp. 1350043 ◽  
Author(s):  
LI GUO ◽  
YUNTING ZHANG ◽  
ZEWEI ZHANG ◽  
DONGYUE LI ◽  
YING LI
Keyword(s):  
Random Walk ◽  
Lung Nodule ◽  
Texture Feature ◽  
Ellipse Fitting ◽  
Lung Nodule Detection ◽  
Random Walk Algorithm ◽  
Fitting Algorithm ◽  
Nodule Detection ◽  
Walk Algorithm ◽  
Automatic Technique

In this paper, we proposed a semi-automatic technique with a marker indicating the target to locate and segment nodules. For the lung nodule detection, we develop a Gabor texture feature by FCM (Fuzzy C Means) segmentation. Given a marker indicating a rough location of the nodules, a decision process is followed by applying an ellipse fitting algorithm. From the ellipse mask, the foreground and background seeds for the random walk segmentation can be automatically obtained. Finally, the edge of the nodules is obtained by the random walk algorithm. The feasibility and effectiveness of the proposed method are evaluated with the various types of the nodules to identify the edges, so that it can be used to locate the nodule edge and its growth rate.

An Improved Iterative Polynomial Fitting Algorithm for Baseline Correction in X-Ray Spectrum

2021 ◽  
Vol 105 ◽  
pp. 90-98
Author(s):  
Xiao Yu Jiang ◽  
Qing Ya Wang ◽  
Mu Qiang Xu ◽  
Jun Hao
Keyword(s):  
Fluorescence Spectrum ◽  
Soil Analysis ◽  
Simulated Data ◽  
Real Data ◽  
Polynomial Fitting ◽  
X Ray ◽  
Iterative Processes ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Fitting In

An iterative polynomial fitting method is proposed for the estimate of the baseline of the X-ray fluorescence spectrum signal. The new method generates automatic thresholds by comparing the X-ray fluorescence spectrum signal with the calculated signal from polynomial fitting in the iterative processes. The signal peaks are cut out consecutively in the iterative processes so the polynomial fitting will finally give a good estimation of the baseline. Simulated data and real data from the soil analysis spectrum are used to demonstrate the feasibility of the proposed method.

New Algorithm of Response Curve for Fitting HDR Image

2019 ◽  
Vol 34 (01) ◽  
pp. 2054001 ◽  
Author(s):  
Shi Qiu ◽  
Xuemei Li ◽  
Yongdong Huang ◽  
Zhengzhou Li ◽  
Xun Chen ◽  
...  
Keyword(s):  
Response Curve ◽  
Image Sequences ◽  
Response Curves ◽  
Time Intervals ◽  
Optimal Response ◽  
Fitting Method ◽  
Fitting Algorithm ◽  
Texture Characteristics ◽  
Pixel Value ◽  
Hdr Image

Based on the process of generating HDR images from LDR image sequences with different light exposures in the same scene, a new fitting method of camera response curves is proposed to solve the problem that the boundary of the fitting algorithm of camera response curves will be blurred and it is difficult to determine and verify the accuracy of the fitting curves. The optimal response curve is fitted by increasing LDR images step by step through considering the pixel value and texture characteristics. In order to validate the fitting effect of curves, we compare the photographed images and the real images in different time intervals on the basis of HDR images and response curves. We use RGB and gray image experiments to compare the current mainstream algorithms and the accuracy of our proposed algorithm can reach 96%, which has robustness.

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