This paper presents our research on calibrating IOP of digital camera with mono-view. First, the Radial Alignment
Constrain (RAC) from Tsai's two-stage calibration is outlined and extended to handle with camera principle point
parameters. Second, we relate principle point to other camera parameters by means of two vanishing points from the
perspective projection of group lines, parallel to World Coordinate System X, Y axis, respectively, and with the relation,
the function-dependence among parameters within the equation derived from extended RAC are eliminated. Third, based
on the extended RAC, a new nonlinear equation for IOP calibration purpose is formulated and optimally solved with
Least Square technique as well as initial value for parameters are determined. Finally, a set of stimulated images
generated with virtual plane grid lines and known camera parameters are used for calibration experiment. The
comparison of proposed approach with Tsai's two-stage calibration is also given in this paper and valuable conclusions
are conducted as well.
Low-cost high-solution imagery is attractive to various applications. This paper presents our research on the automatic
mosaic of high-resolution image sequence from civil Unmanned Aerial Vehicle (UAV). First, the image geometry
distortions resulted from perspective projection are discussed and based on them, the key issue to the mosaic of images
from UAV are described. Second, two common techniques for the registration of remote sensing image, homography
alignment and Thin Plate Spline (TPS) transformation, are outlined, compared and based on their complement with each
other, the integration of two techniques for the mosaic of long-ranged image with geometry distortion is proposed. Third,
one leveled TPS technique is developed to integrate homography with TPS as well as its parameters are estimated with
level transformation or Least Square Technique. Finally, the Mosaic of UAV high-resolution Image sequence is
experimented with proposed approach and valuable conclusions are conducted as well.
This paper presents our research on classifying scattered 3D aerial Lidar height data into ground, vegetable (trees) and
man-made object (buildings) using Support Vector Machine algorithm. To this end, the most basic theory of SVM is first
outlined and with concern to the fact that features are differed in their contribution to classification, Weighted Support
Vector Machine (W-SVM) technique is proposed. Second, four features consist of height, height variation, plane fitting
error and Lidar return intensity are identified for classification purposes. In this step, features are normalized respectively
and their weight that indicates feature's contribution to certain class or multi-class as a whole are calculated and
specified. Third, Based on W-SVM technique, one 1AAA1 solution to multi-class classification is proposed by
integration "one against one" and "one against all" solution together. Finally, the classification results of LIDAR data
with presented technique clearly demonstrate higher classification accuracy and valuable conclusions are given as well.
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