Jinlong Feng

Learn More
Calibration is the process of mapping raw sensor readings into corrected values by identifying and correcting systematic bias. Calibration is important from both off-line and on-line perspectives. Major objectives of calibration procedure include accuracy, resiliency against random errors, ability to be applied in various scenarios, and to address a variety(More)
Calibration is the process of identifying and correcting for the systematic bias component of the error in sensor measurements. Traditionally, calibration has usually been conducted by considering a set of measurements in a single time frame and restricted to linear systems with the assumption of equal-quality sensors and single modality. The basis for the(More)
An articulated model is composed of a set of rigid parts connected by some flexible junctions. The junction, as a critical local feature, provides valuable information for many 3D semantic analysis applications such as feature recognition, semantic segmentation, shape matching, motion tracking and functional prediction. However, efficient description and(More)
Figure 1. Systematic bias and random noise of sensor measurements. ABSTRACT We have developed an on-line calibration scheme that employs a single source as the external stimulus that creates differential sensor readings used for calibration. The technique utilizes the maximal likelihood principle and a nonlinear system optimization solver to derive the(More)
  • 1