More Efficient Methods among Commonly Used Robust Estimation Methods for Similarity Transformation

Yuan Yuan; Yong Hui Ge

doi:10.4028/www.scientific.net/AMR.712-715.2497

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Box Parts Manufacturability Evaluation Based on Feature Model
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Assembly Sequence Planning Based on Connector Structure and Ant Colony Algorithm
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The Research of a Two-Directional Vector-Valued Quarternary Wavelet Packets and Applications in Material Engineering
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More Efficient Methods among Commonly Used Robust Estimation Methods for Robust Unitary Linear Regression
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More Efficient Methods among Commonly Used Robust Estimation Methods for Similarity Transformation
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Research on Technology of Broadband Optical Fiber Access Network System
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A Fast Connection Construction and Update Algorithm
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A Study on CIF-to-BMP Format Transformation Algorithm
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Physical Layer Security Solutions for High Speed Data Networks
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More Efficient Methods among Commonly Used Robust Estimation Methods for Similarity Transformation

Abstract:

mage calibration is a necessary step in image processing. Similarity transformation is a widely used image calibration method. Robust estimation methods are often used to eliminate or weaken the influences of gross errors on image calibration. However, different robust estimation methods have different capabilities in eliminating or weakening gross errors. The current paper employed simulation experiments using different coincident points and the number of gross errors included in the observations to compare the robustness of 13 commonly used robust estimation methods. Results indicated that L1 and GermanMcClure methods are relatively more efficient than other robust estimation methods for image calibration based on similarity transformation.