calibration

问题 I have been using a Samsung Galaxy S3 to work on an app that uses the barometer estimate the user's altitude. I use SensorManager.getAltitude(p0, p), and set p0 equal to the sea-level pressure reported at the nearest airport. I set p equal to the value returned by the barometer. Unfortunately, the altitude returned is systematically 20 meters or so higher than what it actually is. I'm certain that the barometer is returning pressure values that are too low, so SensorManager.getAltitude is

问题 So, I've been struggling with this problem for some time, and haven't had any luck tapping the wisdom of the internets and related SO posts on the subject. I am writing an Android app that uses the ubiquitous Accelerometer, but I seem to be getting an incredible amount of "noise" even while at rest, and can't seem to figure out how to deal with it as my readings need to be relatively accurate. I thought that maybe my phone (HTC Incredible) was dysfunctional, but the sensor seems to work well

问题 I use home to capture by kinect: capture.retrieve( depthMap, CV_CAP_OPENNI_DEPTH_MAP ) capture.retrieve( bgrImage, CV_CAP_OPENNI_BGR_IMAGE ) Now I don't know if I have to calibrate kinect to have depth pixel value correct. That is, if I take a pixel (u, v) from the image RBG, get the correct value of depth taking the pixels (u, v) from the image depth? depthMap.at<uchar>(u,v) Any help is much appreciated. Thanks! 回答1: You can check if registration is on like so: cout << "REGISTRATION " <<

问题 I work on calibration of probabilities. I'm using a probability mapping approach called generalized additive models. The algorithm I wrote is: probMapping = function(x, y, datax, datay) { if(length(x) < length(y))stop("train smaller than test") if(length(datax) < length(datay))stop("train smaller than test") datax$prob = x # trainset: data and raw probabilities datay$prob = y # testset: data and raw probabilities prob_map = gam(Target ~ prob, data = datax, familiy = binomial, trace = TRUE)

问题 I'm using a wide-angle lens (178º Diagonal FOV ) and I'm trying to calibrate it properly using Opencv Calibration module. All the detection and calibration process are working fine, but the result is very poor. I have tried many different configurations: Different set of images Different radial coefficient numbers: 2,3,4,5 even 6.(CV_CALIB_FIX_K1,...,CV_CALIB_FIX_K6 ) Fixing principal point and tangential disortion to 0 (CV_CALIB_FIX_ASPECT_RATIO, CV_CALIB_FIX_PRINCIPAL_POINT) Using expected

问题 I've been working a lot with Photoshop .psd files recently and have been bouncing back and forth a lot to ensure colors look just right on the iPhone. Can anyone offer some tips on calibrating macbook/apple cinema displays in regards to keeping color consistency between Photoshop, the iPhone simulator, and physical iPhones? 回答1: Have a look on LiveView created by Nicholas Zambetti. You need to install it on your iPad/iPhone/iPod Touch and on your Mac. when synced (Mac and device) you can have

问题 Recently, I have been studying the pinhole camera model, but I was confused with the model provided by OpenCV and the " Multiple View geometry in computer vision " textbook. I know that the following photo is a simplified model which switch the position of the image plane and the camera frame. For better illustration and understanding, and taking consideration of the principal point (u0,v0), the relation between two frames is x=f(X/Z)+u0 and y=f(Y/Z)+v0 . However,I was really confused because

问题 I have a python script that uses the calibratecamera2 method to calibrate a camera from a few views of a checker board. After a successful calibration I go after all original points and do some plots and compute again the re-projection error. My surprise is that the reprojection error computed by opencv and mine are a bit different. I found it strange. Am I computing it in a wrong way? obj_points = []# 3d point in real world space. List of arrays img_points = []# 2d points in image plane.

问题 currently i work on calibration of probability. i use the calibration approach, called rescaling algorithm - the source http://lem.cnrs.fr/Portals/2/actus/DP_201106.pdf (page 7). the algorithm i wrote is: rescaling_fun = function(x, y, z) { P_korg = z # yhat_test_prob$BAD P_k_C1 = sum(as.numeric(y) - 1)/length(y) # testset$BAD P_kt_C1 = sum(as.numeric(x) - 1)/length(x) # trainset$BAD P_k_C0 = sum(abs(as.numeric(y) - 2))/length(y) P_kt_C0 = sum(abs(as.numeric(x) - 2))/length(x) P_new <- ((P_k

问题 When I run go_calib_optim on the calibration data I get: Error using - Matrix dimensions must agree. Error in compute_extrinsic_init (line 66) Y = X_kk - (X_mean*ones(1,Np)); Error in comp_ext_calib (line 22) [omckk,Tckk] = compute_extrinsic_init(x_kk,X_kk,fc,cc,kc,alpha_c); Error in go_calib_optim_iter (line 293) comp_ext_calib; Error in go_calib_optim (line 56) go_calib_optim_iter; Error in calibration_script (line 176) go_calib_optim; What could be causing this? Alternatively, is this