Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: An (non-exhaustive) overview of geometric camera calibration (GCC) tools. Supported camera models are mostly explained in Section 3.1 . rad: radial, tan: tangential, GUI: graphical user interface.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Graphic user interfaces of calib.io ( a ), the MATLAB camera calibrator ( b ), the ROS camera calibrator ( c ), and the AgiSoft MetaShape ( d ) for geometric wide-angle camera calibration.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Error distributions of pinhole radial–tangential model parameters and the root mean square (RMS) reprojection errors, by five geometric camera calibration tools, BabelCalib, Camodocal, Kalibr/TartanCalib, the MATLAB calibrator, and the ROS/OpenCV calibrator, on the simulated data for S04525, E1M3518, and BM4218 lenses, each with nine sequences. Note that BabelCalib used a pinhole radial model, resulting in zero values in p 1 and p 2 , which are shown relative to the true values.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Error distributions of KB-8 parameters and the root mean square (RMS) reprojection errors, using five geometric camera calibration tools, BabelCalib, Basalt, Camodocal, Kalibr, and the OpenCV-based ROS calibrator, on the simulated data for BM4218, BM4018, BT2120, and MTV185 lenses, each with nine sequences. The results of the ROS calibrator for the BM4218, BT4018, and MTV185 lenses, and the MATLAB calibrator for the MTV185 lens, were excluded due to persistent failures.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Error distributions of Mei parameters and the root mean square (RMS) reprojection errors, by geometric camera calibration tools, BabelCalib, Basalt, Camodocal, Kalibr, and the OpenCV-based ROS camera calibrator, on the simulated data for BM4018, BT2120, and MTV185 lenses, each with nine sequences. BabalCalib and Basalt used the extended unified camera model (EUCM), while the others used the Mei model. Note that the β of the EUCM is shown together with k 1 of the Mei model relative to k 1 ’s true value. A parameter unavailable to the EUCM, e.g., k 2 , is zero by default, and shown relative to the parameter’s true value. Basalt failed all MTV185 sequences.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Pinhole radial–tangential model parameters and the root mean square (RMS) reprojection errors, by geometric camera calibration tools, BabelCalib, Camodocal, Kalibr, the MATLAB calibrator, and the ROS/OpenCV camera calibrator, on the real datasets captured with S04525, E1M3518, and BM4218 lenses, each with nine sequences. Note that BabelCalib adopted a pinhole radial model. The ROS calibrator failed four out of nine times for the BM4218 dataset.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: KB-8 parameters and the root mean square (RMS) reprojection errors by geometric camera calibration tools, BabelCalib, Basalt, Camodocal, Kalibr, and the ROS/OpenCV calibrator, on the real datasets captured with BM4218, BM4018, BT2120, and MTV185 lenses, each with nine sequences. Basalt failed 7 out of 9 times for both the BM4218 and MTV185 datasets, leading to its small variances.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Geometric Wide-Angle Camera Calibration: A Review and Comparative Study

doi: 10.3390/s24206595

Figure Lengend Snippet: Mei parameters and the root mean square (RMS) reprojection errors by geometric camera calibration tools, BabelCalib with the extended unified camera model (EUCM), Basalt with the EUCM, Camodocal, Kalibr/TartanCalib, and the ROS/OpenCV camera calibrator, on the real data captured with BM4018, BT2120, and MTV185 lenses, each with nine sequences. Note that the EUCM model parameter β is shown together with the Mei parameter k 1 . The unavailable parameters, e.g., k 2 to the EUCM model, are zero by default. Basalt failed 8 out of the 9 MTV185 sequences.

Article Snippet: For cameras with a DAOV ≥ 100 ∘ , the KB-8 model was solved for by using five tools, except for the MATLAB calibrator, which does not support KB-8.

Techniques:

Journal: PLoS ONE

Article Title: Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

doi: 10.1371/journal.pone.0037706

Figure Lengend Snippet: The y-axis of matrix shows the atom type index ( i = 30 protein atom types shown in ) and the x-axis shows the j index for the 32 A i,j features, where j = 1,31 represents the 31 interacting atom types shown in and the 32 nd feature reflects the local geometry of the protein surface. The matrix element ( j,i ) shows the Mann-Whitney U-test p-value in color-code for the two groups of A i,j : one group of A i,j was calculated for the attribute type j around the surface atom type i in the known PPI sites on proteins in the S432 dataset and the other group was calculated for the same attribute type around the non-PPI site atom type i in the same dataset. The p-values were calculated with the Mann-Whitney U-test implemented as the function ranksum in MATLAB. Two sets of data were input to the function and the output p-value is the probability for the two distributions of data to be statistically indistinguishable. The plus(+) sign in the matrix element indicates that the averaged feature value for the PPI site atoms is larger than the averaged feature value for the non-PPI site atoms and the negative(−) is the opposite. The panel on the right-hand-side of the matrix shows the distributions of protein surface atoms in PPI sites (blue) and non-PPI protein surfaces (red) against protein atom type. The data were derived from proteins in S432.

Article Snippet: The Mann-Whitney U-tests were carried out with the statistic tool ranksum in MATLAB ( http://www.mathworks.com/help/toolbox/stats/ranksum.html ).

Techniques: MANN-WHITNEY, Derivative Assay