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Table 1. Summary of the pose estimation results.

in NONLINEAR DIMENSIONALITY REDUCTION TECHNIQUES AND THEIR APPLICATIONS
by unknown authors
"... In PAGE 2: ... Some pose images in our database. [5] Table1 provides a summary of the head pose estimation results using Eigenspace, LLE and Isomap algorithms. Shown are the accuracy (percentage of test images which are correctly classified) and time needed to classify one test image, implemented in Matlab on Intel Pentium Xeon (2.... ..."

Table 1: Localizer results in pose estimation.

in Ad Hoc Networks for Localization and Control
by Aveek Das, John Spletzer, Vijay Kumar, Camillo Taylor 2002
"... In PAGE 6: ... Using these imperfect measurements, we proceeded to apply the three schemes to recover an estimate for the formation pose. Table1 shows a sample localization trial of the initial for- mation (A), emphasizing the parameters of interest for the respective controllers as inferred from the localization data. Results from over 150 simulation trials indicate that the per- formance correlated well with the complexity of the ap- proach.... ..."
Cited by 8

TABLE I COMPARISON OF THE POSE ESTIMATION METHODS.

in Quadrotor Control Using Dual Camera Visual Feedback
by James P. Ostrowski, Camillo J. Taylor 2003
Cited by 5

Table2:PoseEstimatorPerformanceResults

in ABSTRACT Space Vehicle Pose Estimation via Optical Correlation and Nonlinear Estimation
by John M. Rakoczy, Kenneth A. Herren

Table 4. Face Pose Estimation Accuracy

in Kernel Machine Based Learning For Multi-View Face Detection and Pose Estimation
by Stan Z. Li, QingDong Fu, Lie Gu, Bernhard Schölkopf, Yimin Cheng, HongJiag Zhang

Table B.1: Pose estimation inaccuracy.

in unknown title
by unknown authors

Table 1. Pose estimation error rates

in QUERY DRIVEN LOCALIZED LINEAR DISCRIMINANT MODELS FOR HEAD POSE ESTIMATION
by Zhu Li, Yun Fu, Junsong Yuan, Thomas S. Huang, Ying Wu
"... In PAGE 3: ... The proposed solution performs well compared with state of art global graph embedding techniques like LPP. The error rates for pan and tilt angles recognitions are shown in Table1 . Notice that supervised methods, i.... ..."

Table 2: Linear model estimation

in Early estimates of the size of branch-and-bound trees
by Gérard Cornuéjols, Miroslav Karamanov, Yanjun Li 2006
"... In PAGE 9: ...Computational experience (MIPLIB instances) 3 OUR METHOD Table2 compares the size of the measurement tree obtained by the linear model with the actual number of nodes in T. The last column shows the ratio between the two.... ..."
Cited by 2

Table 1. Commanded Mission Poses

in Control Systems Architecture, Navigation, and Communication Research Using the NPS Phoenix Underwater Vehicle
by D. B. Marco, A. J. Healey, R. B. McGhee, D. P. Brutzman, R. Cristi
"... In PAGE 18: ... During execution, all pertinent data was collected, including depth and heading information, all sonar data, and the estimates of position, position rate, and the updates from the sonar. Table1 shows the commanded position and heading comprising the five poses and ... ..."

Table 1: Usability of present day eye-gaze techniques. The information has been gathered from numerous places (Scott amp; Findlay, 1993; Baluja amp; Pomerleau, 1994; Wooding, 1995, among others).

in Eye Controlled Media: Present and Future State
by Arne John Glenstrup, Theo Engell-nielsen 1995
"... In PAGE 46: ... Errors in the program should be corrected. Table1 0: Important problems with the EyeCatcher... In PAGE 57: ...Advantages Disadvantages Keyboard Enables free composition of in- put, precise spelling and precise magnitude input (one can spec- ify (42,17) instead of pointing at the approximate spot) Input rate is low and constrained to character-based descriptions Mouse Enables semi-precise positioning and input of position or shape- based descriptions, and includes a take/drop function (mouse button) Input is constrained to pointing and clicking on menus (non-free compositon) Eye-gaze Input rate is high and shows the point of interest The eyes are continuously oper- ated, do not have a take/drop function and input is constrained to pointing and menu-selection (non-free composition) Speech Enables free composition of input and does not require syntactic knowledge of keyboards Input is ambiguous (homonyms cannot be distinguished) and can disturb the surroundings Table1 1: Communication advantages and disadvantages of the di erent modes of communication from human to computer We believe that the ideal eye-gaze application should use several dimen- sions of the eye-tracking data. As described in section 3 on page 12, the nal eye-movements are caused by many di erent cognitive processes, so di erent aspects of the user apos;s cognitive state are exposed by di erent fea- tures of the gaze pattern.... In PAGE 65: ...Voluntary Time context Change in pupil size ? Short Altered blinking rate ? Short Head orientation + Short Sudden head movements ? Short Gestures + Short Change in heartbeat volume ? Short Uneasiness / nervous movements + Short Voice characteristica + Short Eye contact avoidance + Short Alterations in pulse rate ? Long Breathing rate ? Long Body temperature ? Long Sweat rate ? Long Table1 2: A ection measures within a short time interval and those which demand long time interpretation sampling intervals (cf. gure 12 on the next page).... ..."
Cited by 14
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