In economics, physical capital or just capital is a factor of production (or input into the process of production), consisting of machinery, buildings, computers, and the like. The production function takes the general form Y=f(K, L), where Y is the amount of output produced, K is the amount of capital stock used and L is the amount of labor used. In economic theory, physical capital is one of the three primary factors of production, also known as inputs in the production function. The others are natural resources (including land), and labor — the stock of competences embodied in the labor force. 'Physical' is used to distinguish physical capital from human capital (a result of investment in the human agent)), circulating capital, and financial capital.[1][2] 'Physical capital' is fixed capital, any kind of real physical asset that is not used up in the production of a product.

Usually the value of land is not included in physical capital as it is not a reproducible product of human activity.

Fs.release(); // close Settings file For this I've used simple OpenCV class input operation. Totally Rad 2 Rapidshare Downloads. After reading the file I've an additional post-processing function that checks validity of the input.

Only if all inputs are good then goodInput variable will be true. • Get next input, if it fails or we have enough of them - calibrate After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images then we run the calibration process. In case of image we step out of the loop and otherwise the remaining frames will be undistorted (if the option is set) via changing from DETECTION mode to the CALIBRATED one. } Depending on the type of the input pattern you use either the or the function. For both of them you pass the current image and the size of the board and you'll get the positions of the patterns.

Mar 13, 2012. OpenCV uses mulfple views of a planar object rather than one view of a 3‐D object (J. +CV_CALIB_CB_NORMALIZE IMAGE ). Corners are detected and recorded starting from the lower right corner of the chessboard. First Corner. Homography is only defined up to a scale factor. 2D pattern chessboard was selected as object in this calibration test. The calibration process starts with capturing the images of the chessboard pattern by posing the chessboard at different angle in front of the cameras. In addition a new program. (a) Raw stereo image taken.

Furthermore, they return a boolean variable which states if the pattern was found in the input (we only need to take into account those images where this is true!). Then again in case of cameras we only take camera images when an input delay time is passed. This is done in order to allow user moving the chessboard around and getting different images. Similar images result in similar equations, and similar equations at the calibration step will form an ill-posed problem, so the calibration will fail. For square images the positions of the corners are only approximate. We may improve this by calling the function. It will produce better calibration result.

After this we add a valid inputs result to the imagePoints vector to collect all of the equations into a single container. Finally, for visualization feedback purposes we will draw the found points on the input image using function. } The calibration and save Because the calibration needs to be done only once per camera, it makes sense to save it after a successful calibration.