Abstract: A method and apparatus for calibrating an image capture device are provided. The method includes capturing one or more of a single or Multiview image set by the image capture device, detecting one or more calibration features in each set by a processor, initializing each of the one or more calibration parameters a corresponding default value, extracting one or more relevant calibration parameters, computing an individual cost term for each of the identified relevant calibration parameters, and scaling each of the relevant cost terms. The method continues with combining all the cost terms once each of the calculated relevant cost terms have been scaled, determining if the combination of the cost terms has been minimized, adjusting the calibration parameters if it is determined that that the combination of the cost terms has not been minimized, and returning to the step of extracting one or more of the relevant calibration parameters.

Abstract: A method and apparatus for calibrating an image capture device are provided. The method includes capturing one or more of a single or Multiview image set by the image capture device, detecting one or more calibration features in each set by a processor, initializing each of the one or more calibration parameters a corresponding default value, extracting one or more relevant calibration parameters, computing an individual cost term for each of the identified relevant calibration parameters, and scaling each of the relevant cost terms. The method continues with combining all the cost terms once each of the calculated relevant cost terms have been scaled, determining if the combination of the cost terms has been minimized, adjusting the calibration parameters if it is determined that that the combination of the cost terms has not been minimized, and returning to the step of extracting one or more of the relevant calibration parameters.

Abstract: Multiview calibration is essential for accurate three-dimensional computation. However, multiview calibration can not be accurate enough because of the tolerances required in some of the intrinsic and extrinsic parameters that are associated with the calibration process, along with fundamental imperfections that are associated with the manufacturing and assembly process itself. As a result, residual error in calibration is left over, with no known methods to mitigate such errors. Residual error mitigation is presented in this work to address the shortcomings that are associated with calibration of multiview camera systems. Residual error mitigation may be performed inline with a given calibration approach, or may be presented as a secondary processing step that is more application specific. Residual error mitigation aims at modifying the original parameters that have been estimated during an initial calibration process.

Abstract: A method and system for calibration parameter validation. The method includes performing a camera calibration generating a calibration result, applying a coarse pass-fail criteria to the calibration result and rejecting the camera if the calibration result does not pass the coarse pass fail-criteria. The method further includes applying a fine pass-fail criteria based upon at least information stored at a central repository to the calibration result if the calibration result passes the coarse pass-fail criteria, rejecting the camera if the calibration result does not pass the fine pass fail-criteria, accepting the camera if the calibration result passes the fine pass fail-criteria, and entering the calibration result into the central repository. The information stored at the central repository includes key performance indicator/key performance metric information for one or more measured parameters of the calibration result.

Abstract: Multiview calibration is essential for accurate three-dimensional computation. However, multiview calibration can not be accurate enough because of the tolerances required in some of the intrinsic and extrinsic parameters that are associated with the calibration process, along with fundamental imperfections that are associated with the manufacturing and assembly process itself. As a result, residual error in calibration is left over, with no known methods to mitigate such errors. Residual error mitigation is presented in this work to address the shortcomings that are associated with calibration of multiview camera systems. Residual error mitigation may be performed inline with a given calibration approach, or may be presented as a secondary processing step that is more application specific. Residual error mitigation aims at modifying the original parameters that have been estimated during an initial calibration process.

Abstract: A method and system for calibration parameter validation. The method includes performing a camera calibration generating a calibration result, applying a coarse pass-fail criteria to the calibration result and rejecting the camera if the calibration result does not pass the coarse pass fail-criteria. The method further includes applying a fine pass-fail criteria based upon at least information stored at a central repository to the calibration result if the calibration result passes the coarse pass-fail criteria, rejecting the camera if the calibration result does not pass the fine pass fail-criteria, accepting the camera if the calibration result passes the fine pass fail-criteria, and entering the calibration result into the central repository. The information stored at the central repository includes key performance indicator/key performance metric information for one or more measured parameters of the calibration result.