Disparity refinement is an important step to enhance the accuracy of stereo matching. This paper extends the scheme of a recent successful approach, namely the nonlocal disparity refinement algorithm, to exploit the initial disparity map in the aggregation phase of disparity refinement, in addition to the information of spatial distance and intensity difference. In addition, we propose a constraint function applied to the matching cost that constrains the scope of dissimilarity measures to further improve the accuracy of disparity refinement. Extensive experimental comparisons with several state-of-the-art methods using the Middlebury Stereo Evaluation version 3 datasets show that the proposed scheme has a great advantage in disparity refinement.
This work investigates the calibration of a stereo vision system based on two PTZ (Pan-Tilt-Zoom) cameras. As the
accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationships between rotation
axes of the cameras, the major concern is the development of an effective and systematic way to obtain these
relationships. We derived a complete geometric model of the dual-PTZ-camera system and proposed a calibration
procedure for the intrinsic and external parameters of the model. The calibration method is based on Zhang’s approach
using an augmented checkerboard composed of eight small checkerboards, and is formulated as an optimization problem
to be solved by an improved particle swarm optimization (PSO) method. Two Sony EVI-D70 PTZ cameras were used
for the experiments. The root-mean-square errors (RMSE) of corner distances in the horizontal and vertical direction are
0.192 mm and 0.115 mm, respectively. The RMSE of overlapped points between the small checkerboards is 1.3958 mm.
In this paper, we propose an application of augmented reality technology for targeting tumors or anatomical
structures inside the skull. The application is a combination of the technologies of MonoSLAM (Single Camera
Simultaneous Localization and Mapping) and computer graphics. A stereo vision system is developed to construct
geometric data of human face for registration with CT images. Reliability and accuracy of the application is enhanced by
the use of fiduciary markers fixed to the skull. The MonoSLAM keeps track of the current location of the camera with
respect to an augmented reality (AR) marker using the extended Kalman filter. The fiduciary markers provide reference
when the AR marker is invisible to the camera. Relationship between the markers on the face and the augmented reality
marker is obtained by a registration procedure by the stereo vision system and is updated on-line. A commercially
available Android based tablet PC equipped with a 320×240 front-facing camera was used for implementation. The
system is able to provide a live view of the patient overlaid by the solid models of tumors or anatomical structures, as
well as the missing part of the tool inside the skull.
This paper proposes a scheme for finding the correspondence between uniformly spaced locations on the images of
human face captured from different viewpoints at the same instant. The correspondence is dedicated for 3D
reconstruction to be used in the registration procedure for neurosurgery where the exposure to projectors must be
seriously restricted. The approach utilizes structured light to enhance patterns on the images and is initialized with the
scale-invariant feature transform (SIFT). Successive locations are found according to spatial order using a parallel
version of the particle swarm optimization algorithm. Furthermore, false locations are singled out for correction by
searching for outliers from fitted curves. Case studies show that the scheme is able to correctly generate 456 evenly
spaced 3D coordinate points in 23 seconds from a single shot of projected human face using a PC with 2.66 GHz Intel
Q9400 CPU and 4GB RAM.
In this paper, we propose a calibration process for the intrinsic and extrinsic parameters of dual-PTZ camera systems.
The calibration is based on a complete definition of six coordinate systems fixed at the image planes, and the pan and tilt
rotation axes of the cameras. Misalignments between estimated and ideal coordinates of image corners are formed into
cost values to be solved by the Nelder-Mead simplex optimization method. Experimental results show that the system is
able to obtain 3D coordinates of objects with a consistent accuracy of 1 mm when the distance between the dual-PTZ camera
set and the objects are from 0.9 to 1.1 meters.
This paper presents the development of a three-dimensional environment reconstruction system using a laser range finder.
The original design of URG-04LX laser range finder, provided by Hokuyo Inc., is efficient in providing two-dimensional
distance information. To enhance the capability of the device, we developed a rotation mechanism to provide it a sweep
motion for stereo data collection. Geometric equations are derived that includes parameters of misalignment that are
unavoidable in manufacturing and assembling. The parameters are calibrated according to practical data measurement of
three relatively-perpendicular planes. The calibration is formulated as an optimization problem solved using the Nelder-
Mead simplex algorithm. Validity of the calibration scheme is demonstrated by the reconstruction of several real-world