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Friday Presentation Comments

Friday Presentation Comments. Vitaliy Orekhov. SLM Distortion Calibration. Compare SLM distortion calibration to Zhang calibration method.

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Friday Presentation Comments

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  1. Friday Presentation Comments Vitaliy Orekhov

  2. SLM Distortion Calibration Compare SLM distortion calibration to Zhang calibration method. • Zhang [32] achieved a root-mean-square (RMS) error on his publicly available dataset [33] of 0.335, where he modeled radial distortion only. This corresponds to an MSE of approximately 0.1122. MSE for real data with original and reversed RDDD distortion model. MSE for real data with original and reversed LPDD distortion model. MSE Original Distortion Model [pixels] MSE Modified Distortion Model [pixels] MSE Original Distortion Model [pixels] MSE Modified Distortion Model [pixels]

  3. Model Complexity and Computation Cost Computation cost with LPDD • Zhang’s Data Tangential Coef. Radial Coef.

  4. Model Complexity and Computation Cost Computation cost with RDDD • Zhang’s Data Tangential Coef. Radial Coef.

  5. Model Complexity and Computation Cost Computation cost with SLM distortion calibration • Zhang’s Data • RDDD model Tangential Coef. Radial Coef.

  6. Model Complexity Selection • Which criterion to use? • [Gheissari03] say that many factors affect the suitability of criterions for employment in computer vision application. • They suggest that the most important factors are the application, data size, noise, and model library. Model Selection with Synthetic Data

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