Automatic Defect Extraction and Segmentation in Welding Seam Based on X-Ray Images

In light of the characteristic of thin-wall weld joint in X-ray image, Flaw-edge extraction algorithm and image enhancement algorithm which is based on mathematical morphology are proposed in the study of flaw extraction technique. Therefore, the area of flaw and background can be removed successfully. On this basis, there are two algorithms to identify different flaw types: one is that spatial domain transform to extract flaw edge for clack, the other one is mathematical morphology which is combined with iteration threshold to extract flaw edge for pore; Experimental results show that both of algorithms can implement flaw extraction and segmentation automatically, which is lay a good foundation for flaw feature parameter extraction and recognition.

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Feature parameter extraction algorithm for the large-scale complex structure tank based on 3D laser scanning volume measurement

2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI) ◽

10.1109/icemi46757.2019.9101698 ◽

2019 ◽

Author(s):

Cheng Zhongyi ◽

Yang Maoji ◽

Kang Bo ◽

Bian Xingyuan ◽

Cui Junning

Keyword(s):

Laser Scanning ◽

Large Scale ◽

Complex Structure ◽

Parameter Extraction ◽

Volume Measurement ◽

3D Laser Scanning ◽

Extraction Algorithm ◽

Feature Parameter

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Improved Window Function in the Application of MFCC Feature Parameter Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.3703 ◽

2014 ◽

Vol 556-562 ◽

pp. 3703-3706

Author(s):

Le Qiang Bai ◽

Xue Wei Zhang

Keyword(s):

Recognition Rate ◽

Parameter Extraction ◽

Side Lobe ◽

Window Function ◽

Main Lobe ◽

Impact Function ◽

Extraction Algorithm ◽

Feature Parameter ◽

Lobe Width ◽

Kaiser Window

In view of spectrum leakage and the contradictory problem of spectrum accuracy of main lobe and reducing spectrum leakage, MFCC algorithm based on improved window function is proposed. Improved window function is based on the mathematical analysis of Kaiser window, and under the condition of finite sampling points minuses weighted impact function where is at the frequencies that side lobe peaks of correspond to. The amplitude of improved window compared with Kaiser window is smaller, and main lobe width is the same, solving the conflicting problem of main lobe width and side lobe amplitude and reducing spectrum leakage. The experimental results show that speech recognition rate of MFCC feature parameter extraction algorithm based on improved window function is better than Kaiser window and Hamming window.

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An Improved Feature Parameter Extraction Algorithm of Composite Detection Method Based on the Fusion Theory

Journal of Sensors ◽

10.1155/2021/8898991 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhou Ying ◽

Jin Heli ◽

Liu Banteng ◽

Chen Yourong

Keyword(s):

Eddy Current ◽

Dispersion Model ◽

Parameter Extraction ◽

Quantitative Detection ◽

Differential Signal ◽

Feature Parameters ◽

Pulsed Eddy Current ◽

Extraction Algorithm ◽

Feature Parameter ◽

Subsurface Defects

An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D-S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections.

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Edge Extraction Algorithm using Linear Prediction Model on Dental X-ray Images

International Journal of Computer Applications ◽

10.5120/17632-8017 ◽

2014 ◽

Vol 100 (19) ◽

pp. 9-14 ◽

Cited By ~ 1

Author(s):

Gayathri V ◽

Hema P Menon ◽

K A Narayanankutty

Keyword(s):

Prediction Model ◽

Linear Prediction ◽

Edge Extraction ◽

X Ray ◽

Extraction Algorithm ◽

Linear Prediction Model

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Camera Calibration Method Based on Circle Plane Board

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.475-476.184 ◽

2013 ◽

Vol 475-476 ◽

pp. 184-187

Author(s):

Wen Guo Li ◽

Shao Jun Duan

Keyword(s):

Camera Calibration ◽

Calibration Method ◽

Lens Distortion ◽

Edge Extraction ◽

Circle Plane ◽

Circle Centre ◽

Extraction Algorithm ◽

Calibration Algorithm ◽

Characteristic Points ◽

Distortion Parameters

We present a camera calibration method based on circle plane board. The centres of circles on plane are regarded as the characteristic points, which are used to implement camera calibration. The proposed calibration is more accurate than many previous calibration algorithm because of the merit of the coordinate of circle centre being obtained from thousand of of edge pionts of ellipse, which is very reliable to image noise caused by edge extraction algorithm. Experiments shows the proposed algorithm can obtain high precise inner parameters, and lens distortion parameters.

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