| /external/opencv3/modules/photo/src/ |
| D | fast_nlmeans_denoising_invoker_commons.hpp | 318 static inline void f(IT* estimation, IT* weights_sum, WT weight, T p) in f()
320 estimation[0] += (IT)weight * p; in f()
327 static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 2> p) in f()
329 estimation[0] += (IT)weight * p[0]; in f()
330 estimation[1] += (IT)weight * p[1]; in f()
337 static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 3> p) in f()
339 estimation[0] += (IT)weight * p[0]; in f()
340 estimation[1] += (IT)weight * p[1]; in f()
341 estimation[2] += (IT)weight * p[2]; in f()
348 static inline void f(IT* estimation, IT* weights_sum, WT weight, Vec<ET, 4> p) in f()
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| D | fast_nlmeans_denoising_invoker.hpp | 226 IT estimation[pixelInfo<T>::channels], weights_sum[pixelInfo<WT>::channels]; in operator ()() local
228 estimation[channel_num] = 0; in operator ()()
241 incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p); in operator ()()
245 divByWeightsSum<IT, UIT, pixelInfo<T>::channels, pixelInfo<WT>::channels>(estimation, in operator ()()
247 dst_.at<T>(i,j) = saturateCastFromArray<T, IT>(estimation); in operator ()()
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| D | fast_nlmeans_multi_denoising_invoker.hpp | 246 IT estimation[pixelInfo<T>::channels], weights_sum[pixelInfo<WT>::channels]; in operator ()() local
248 estimation[channel_num] = 0; in operator ()()
267 incWithWeight<T, IT, WT>(estimation, weights_sum, weight, p); in operator ()()
272 divByWeightsSum<IT, UIT, pixelInfo<T>::channels, pixelInfo<WT>::channels>(estimation, in operator ()()
274 dst_.at<T>(i,j) = saturateCastFromArray<T, IT>(estimation); in operator ()()
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/estimation/ |
| D | EstimationProblem.java | 18 package org.apache.commons.math.estimation;
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| D | Estimator.java | 18 package org.apache.commons.math.estimation;
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| D | EstimationException.java | 18 package org.apache.commons.math.estimation;
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| D | WeightedMeasurement.java | 18 package org.apache.commons.math.estimation;
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| D | EstimatedParameter.java | 18 package org.apache.commons.math.estimation;
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| D | SimpleEstimationProblem.java | 18 package org.apache.commons.math.estimation;
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| D | GaussNewtonEstimator.java | 18 package org.apache.commons.math.estimation;
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| D | AbstractEstimator.java | 18 package org.apache.commons.math.estimation;
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| /external/webrtc/webrtc/tools/loopback_test/ |
| D | README | 4 turn. For now the test is used to analyse bandwidth estimation and get records
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| /external/iptables/extensions/ |
| D | libxt_RATEEST.man | 1 The RATEEST target collects statistics, performs rate estimation calculation
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| /external/deqp/doc/testspecs/GLES2/ |
| D | performance.compiler.txt | 27 + Front-end time estimation with invalid shaders
81 Overview for invalid shader cases (front-end time estimation):
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| /external/opencv3/doc/tutorials/calib3d/ |
| D | table_of_content_calib3d.markdown | 31 Real time pose estimation of a textured object using ORB features, FlannBased matcher, PnP
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| /external/opencv3/doc/tutorials/calib3d/real_time_pose/ |
| D | real_time_pose.markdown | 1 Real Time pose estimation of a textured object {#tutorial_real_time_pose}
5 The most elemental problem in augmented reality is the estimation of the camera pose respect of an
25 - Pose estimation using PnP + Ransac.
376 -# **Pose estimation using PnP + Ransac**
388 For the camera pose estimation I have implemented a *class* **PnPProblem**. This *class* has 4
428 … the estimation method will be different. In the case that we want to make a real time application,
431 … surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this this
770 The following videos are the results of pose estimation in real time using the explained detection
791 You can watch the real time pose estimation on the [YouTube
796 <iframe title="Pose estimation of textured object using OpenCV" width="560" height="349" src="http:…
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| /external/opencv3/doc/tutorials/calib3d/camera_calibration_square_chess/ |
| D | camera_calibration_square_chess.markdown | 14 Pose estimation
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| /external/opencv3/doc/ |
| D | opencv.bib | 117 title = {High accuracy optical flow estimation based on a theory for warping},
249 title = {Two-frame motion estimation based on polynomial expansion},
389 title = {TV-L1 optical flow estimation},
811 …title = {Efficient adaptive density estimation per image pixel for the task of background subtract…
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| /external/opencv3/doc/py_tutorials/py_feature2d/py_feature_homography/ |
| D | py_feature_homography.markdown | 26 good matches which provide correct estimation are called inliers and remaining are called outliers.
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| /external/ceres-solver/docs/source/ |
| D | bibliography.rst | 72 estimation of nonlinear parameters**, *J. SIAM*, 11(2):431–441,
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| D | version_history.rst | 13 #. Added ``EIGEN_SPARSE_QR`` algorithm for covariance estimation using
22 #. The ``SPARSE_CHOLESKY`` algorithm for covariance estimation has
26 #. The ``SPARSE_QR`` algorithm for covariance estimation has been
65 #. Homography estimation example from Blender demonstrating the use of
228 #. Sparse and dense covariance estimation.
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| D | features.rst | 70 * **Covariance estimation** - Evaluate the sensitivity/uncertainty of
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| /external/autotest/client/profilers/powertop/src/po/ |
| D | fr.po | 244 msgstr "Consommation électrique (estimation ACPI sur 5 minutes) : %5.1f W (%3.1f heures restantes)"
249 msgstr "Consommation électrique (estimation ACPI) : %3.1fW (%3.1f heures)"
587 msgstr "Pas d'estimation ACPI disponible pour la consommation électrique"
592 msgstr "Pas d'estimation de consommation électrique disponible"
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| /external/iproute2/doc/ |
| D | nstat.sgml | 98 estimation loses in quality.
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| /external/opencv3/doc/py_tutorials/py_video/py_bg_subtraction/ |
| D | py_bg_subtraction.markdown | 106 This algorithm combines statistical background image estimation and per-pixel Bayesian segmentation.
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