A Stratified Optimization Method for Camera Self-Calibration

2012 ◽  
Vol 580 ◽  
pp. 248-252
Author(s):  
Qian Sun ◽  
Dong Xu
Keyword(s):  
Aspect Ratio ◽  
Accurate Result ◽  
Focal Length ◽  
Optimization Method ◽  
Singular Value ◽  
Optimization Approach ◽  
Point Location ◽  
Principal Point ◽  
Self Calibration ◽  
Value Decomposition

We present an efficient stratified optimization approach for self-calibration of a camera in the case that its focal length and the principal point location are unknown. Generally we can assume that the two views are of the same focal length, and the pixels are nearly perfectly rectangular, also it is possible to know the aspect ratio rather accurately. In our approach, we use singular value decomposition to solve a modified Kruppa Equation to derive the focal length with the supposition that the principal point is at the center of the image, and perform an exhaustive search for the principal point near the center of the image to minimize a cost function. We can get a much accurate result with the optimized principal point location.

scholarly journals Optimization Method for Solution Model of Laser Tracker Multilateration Measurement

2016 ◽  
Vol 16 (4) ◽  
pp. 205-210 ◽  
Author(s):  
Hongfang Chen ◽  
Zhi Tan ◽  
Zhaoyao Shi ◽  
Huixu Song ◽  
Hao Yan
Keyword(s):  
Coordinate Measuring Machine ◽  
Optimization Method ◽  
Solution Model ◽  
Laser Tracker ◽  
Accuracy Control ◽  
Self Calibration ◽  
Negative Effect ◽  
Measuring Machine ◽  
Measurements Errors ◽  
Value Decomposition

Abstract Multilateration measurement using laser trackers suffers from a cumbersome solution method for high-precision measurements. Errors are induced by the self-calibration routines of the laser tracker software. This paper describes an optimization solution model for laser tracker multilateration measurement, which effectively inhibits the negative effect of this self-calibration, and further, analyzes the accuracy of the singular value decomposition for the described solution model. Experimental verification for the solution model based on laser tracker and coordinate measuring machine (CMM) was performed. The experiment results show that the described optimization model for laser tracker multilateration measurement has good accuracy control, and has potentially broad application in the field of laser tracker spatial localization.

scholarly journals An Optimization Method for Hyperspectral Endmember Extraction Based on K-SVD

2019 ◽  
Vol 85 (12) ◽  
pp. 879-887
Author(s):  
Xiaoxiao Feng ◽  
Luxiao He ◽  
Ya Zhang ◽  
Yun Tang
Keyword(s):  
Singular Value Decomposition ◽  
Background Noise ◽  
Optimization Method ◽  
Extraction Methods ◽  
Singular Value ◽  
Endmember Extraction ◽  
Partially Observed ◽  
Mixed Pixels ◽  
Ground Object ◽  
Value Decomposition

Mixed pixels are common in hyperspectral imagery (<small>HSI</small>). Due to the complexity of the ground object distribution, some end-member extraction methods cannot obtain good results and the processes are complex. Therefore, this paper proposes an optimization method for <small>HSI</small> endmember extraction, which improves the accuracy of the results based on K-singular value decomposition (<small>K-SVD</small>). The proposed method comprises three core steps. (1) Based on the contribution value of initial endmembers, partially observed data selected according to the appropriate confidence participate in the calculation. (2) Construction of the error model to eliminate the background noise. (3) Using the <small>K-SVD</small> to perform column-by-column iteration on the endmembers to achieve the overall optimality. Experiments with three real images are applied, demonstrating the proposed method can improve the overall endmember accuracy by 15.1%–55.7% compared with the original methods.

Frame Reconstruction with Missing Data from Multiple Images

2012 ◽  
Vol 239-240 ◽  
pp. 1158-1164
Author(s):  
Guang Yu Luan ◽  
Xue Dong Zhu ◽  
Ai Chuan Li ◽  
Zhen Su Lv ◽  
Ren Sheng Che
Keyword(s):  
Missing Data ◽  
Focal Length ◽  
Projective Reconstruction ◽  
Principal Point ◽  
Multiple Images ◽  
Missing Data Problem ◽  
Data Problem ◽  
Value Decomposition ◽  
Projection Matrices ◽  
Projective Shape

To solve the missing data problem that is caused by reasons, such as occlusion, frame reconstruction by a two-level strategy in multiple images was considered. The method first performed a projective reconstruction combining singular value decomposition (SVD) and subspace method with missing data, which estimated projective shape, projection matrices, projective depths and missing data iteratively. Then it converted the projective solution to a Euclidean one with the unknown focal length and the constant principal point by enforcing constraints. Using the constraints and the fact that scale measurement matrix can recover numberless projection matrices and point matrices, the set equations of the transformation matrix from the projective reconstruction to Euclidean reconstruction were obtained. Experimental results using real images are provided to illustrate the performance of the proposed method.

scholarly journals THE SUPPORT VECTOR MACHINE PARAMETER OPTIMIZATION METHOD BASED ON ADAPTIVE ELITIST DIFFERENTIAL EVOLUTION ALGORITHM AND ITS APPLICATION TO ROLLER BEARING FAULT DIAGNOSIS

2022 ◽  
Vol 36 (06) ◽  
Author(s):  
HUNGLINH AO ◽  
THANHHANG NGUYEN ◽  
V.HO HUU ◽  
TRANGTHAO NGUYEN
Keyword(s):  
Singular Value Decomposition ◽  
Differential Evolution ◽  
Parameter Optimization ◽  
Roller Bearing ◽  
Optimization Method ◽  
Singular Value ◽  
Superior Performance ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Value Decomposition

SVM parameters have serious effects on the accuracy rate of classification result. Tuning SVM parameters is always a challenge for scientists. In this paper, a SVM parameter optimization method based on Adaptive Elitist Differential Evolution (AeDE-SVM) is proposed. Furthermore, AeDE-SVM is applied to diagnose roller bearing fault by using complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) techniques. First, original acceleration vibration signals are decomposed into Intrinsic Mode Function (IMFs) by using CEEMD method. Second, initial feature matrices are extracted from (IMFs) by singular value decomposition (SVD) techniques to obtain single values. Third, these values serve as input vector for AeDE-SVM classifier. The results show that the combination of AeDE-SVM classifiers and the CEEMD-SVD method obtains higher classification accuracy and lower cost time compared to other methods. In this paper, the roller bearing vibration signals were used to evaluate the proposed method. The experimental results showed that the superior performance compared to other SVM parameter optimization techniques and successfully recognized different fault types of roller bearing during its operation.

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