High-quality real-time stereo matching has the potential to enable various computer vision applications including semi-automated robotic surgery, tele-immersion, and threedimensional video surveillance. A novel real-time stereo matching method is presented that uses a two-pass approximation of adaptive support-weight aggregation, and a low-complexity iterative disparity refinement technique. Through an evaluation of computationally efficient approaches to adaptive supportweight cost aggregation, it is shown that the two-pass method produces an accurate approximation of the support weights while greatly reducing the complexity of aggregation. The refinement technique, constructed using a probabilistic framework, incorporates an additive term into matching cost minimization and facilitates iterative processing to improve the accuracy of the disparity map. This method has been implemented on massively parallel high-performance graphics hardware using the CUDA computing engine. Results show that the proposed method is the most accurate among all of the real-time stereo matching methods listed on the Middlebury stereo benchmark.

The ability to produce accurate real-time 3D models of the operating field is a significant advancement toward augmented reality in minimally invasive surgery. An imaging system with this capability will potentially transform surgery by helping novice and expert surgeons alike to delineate variance in internal anatomy accurately.

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A dual-network Cyber-physical Networking (CPN) testbed developed at the University of Nebraska-Lincoln is described. The CPN testbed consists of two geographically disparate wireless sensor networks connected by a traditional TCP/IP network and enables peer-to-peer communication between each sensor in the network. The functionality of the testbed is enhanced by a range of software tools that support remote programming and network monitoring, and real-time visualization of sensor data. The resulting architecture supports easy deployment and evaluation of applications for both traditional and interconnected wireless sensor networks. To demonstrate the features of this testbed, a novel ping application was developed and deployed as a proof-of-concept application for peer-to-peer communication in geographically separated wireless sensor networks. Experimental evaluations of the ping application yield insight into the communications overhead that can be expected in future applications of peer-to-peer interconnected sensor networks.

Stereo matching algorithms are nearly always designed to find matches between a single pair of images. A method is presented that was specifically designed to operate on sequences of images. This method considers the cost of matching image points in both the spatial and temporal domain. To maintain real-time operation, a temporal cost aggregation method is used to evaluate the likelihood of matches that is invariant with respect to the number of prior images being considered. This method has been implemented on massively parallel GPU hardware, and the implementation ranks as one of the fastest and most accurate real-time stereo matching methods as measured by the Middlebury stereo performance benchmark.