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Well, I didn t figure it out We looked at everything the program, the specification, the equipment Everything was within the tolerance limits defined by the design engineers and we couldn t find a reason for the misalignment We were frustrated I paused as George and Sid nodded knowingly Then I continued Brian, the engineer, looked at the program for most of the night but couldn t find any fault in the logic Jason, the press operator, showed me the dies and the setup tools he used and we couldn t find anything wrong there either Then, around 9 pm, we all sat down for some coffee I asked Jason to tell me what had been done on the process over the past 20 years Jason explained that he d been running the process since it was started He reached into his back pocket and pulling out a small notebook What a surprise! He had kept process notes on everything that had been done since the beginning I asked if I could take a quick look at his notes They were particularly well laid out and very complete I didn t see anything that would lead me to believe that the process had been disrupted in any way I picked up my cup and finished my coffee A strange look came over Jason s face You know, he started slowly, there was one thing It s probably not really important, but when we were setting up the process, we couldn t be sure which side of the die was supposed to be facing up The process supervisor came out with the engineering team and measured the die and all the locator pins and they decided that the die was symmetric The engineers said that since the die was equal on all sides it didn t really matter which side faced up We marked the die so we could be sure we always do it exactly the same way and that s how we ve been doing it each time we set up the machine I looked at Jason and he read my mind We ran back to set up the machine and run parts, not wanting to wait until the next day to check our theory.

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This chapter will present methods for calibrating cameras and depth measurement systems such as binocular stereo and range cameras The machine vision algorithms developed in previous chapters involved the extraction of measurements within the image plane Measurements, such as the location of an edge or the centroid of a region, were made in the coordinate system of the image array We assumed that image plane coordinates could be determined from image locations using simple formulas based on the premise that the location of the central axis of projection in the image plane (the principal point) was at the center of the image We also assumed that the distance of the image plane from the center of projection (the camera constant) was known This chapter will cover the calibration problems associated with these assumptions Measurements based on the simple image geometry assumed in previous discussions can be corrected to true image plane coordinates using the solution to the interior orientation problem, covered in Section 129 The position and orientation of a camera in the scene can be determined by solving the exterior orientation problem, covered in Section 128 The solution to the interior and exterior orientation problems provides an overall solution to the camera calibration problem that relates the location of pixels in the image array to points in the scene Camera calibration is covered in Section 1210 The relative orientation problem for calibrating the relationship between two cameras for binocular stereo is covered in Section 124, the method for computing depth measurements from binocular stereo disparities is explained in Section 126, and the absolute orientation problem for converting depth measurements in viewer-centered coordinates to an absolute 309

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How to write a function to convert PDF to Excel in C# / . Net Core for ...
Net is a library for developers to convert PDF to Word, RTF, DOC and Text. Allows to extract ... Net application: C# , VB.Net, Silverlight, J#, ColdFusion, ASP . Net .

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coordinate system for the scene is covered in Section 123 Several different coordinate systems encountered in camera calibration are described in Section 121, and the mathematics of rigid body transformations is covered in Section 122 The mathematics behind solutions to the problems of calibrating cameras and range sensors has been developed in the field of photogrammetry Photogrammetry provides a collection of methods for determining the position and orientation of cameras and range sensors in the scene and relating camera positions and range measurements to scene coordinates There are four calibration problems in photogrammetry:

(Beall & Jaffe, Concrete and Masonry Databook, McGraw-Hill, 2003)

Absolute orientation determines the transformation between two coordinate systems or the position and orientation of a range sensor in an absolute coordinate system from the coordinates of calibration points Relative orientation determines the relative position and orientation between two cameras from projections of calibration points in the scene Exterior orientation determines the position and orientation of a camera in an absolute coordinate system from the projections of calibration points in the scene Interior orientation determines the internal geometry of a camera, including the camera constant, the location of the principal point, and corrections for lens distortions

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pdf to excel conversion | The ASP.NET Forums
i want to convert my pdf to excel programmatically using .net c# ... You can try iTextSharp to read the content of the PDF document and then use ...

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These calibration problems are the classic problems in photogrammetry and originated with the techniques used to create topographic maps from aerial images In addition to the four basic calibration problems, photogrammetry also deals with the problem of determining the position of points in the scene from binocular stereo disparities and provides methods for resampling stereo images so that the epipolar lines correspond to image rows All of the photogrammetric problems for determining the transformation between coordinate systems assume that a set of conjugate pairs is available The conjugate pairs are obtained by matching feature points between views These matches must be correct since the classic methods of photogrammetry use least-squares criteria and are very sensitive to outliers due to mismatched features In fact, calibration algorithms can be ill conditioned, and one should

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