David M. Chelberg

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We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels.
This paper presents three case-based reasoning (CBR) prototypes developed for the RoboCats, a team of five soccer playing robots in the RoboCup small size league. CBR is used to help the RoboCats plan individual moves and team strategies, as well as to model the world of the playing field. More specifically, the case-based reasoners position the goalie,(More)
This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resourceconstrained situations and support optimization(More)
A new algorithm for ray tracing generalized cylinders whose axis is an arbit:rary three dimensional space curve and whose cross-sectional contour can be varied accortding to a general sweeping rule is presented. The only restriction placed on the class of generalized cylinders that can be ray-traced is that the sweeping rule of the generalized cylinder must(More)
This paper describes the application of a first order regularization technique to the problem of reconstruction of visible surfaces. Our approach is a computationally efficient first order method that simultaneously achieves approximate in\ nriance and preservation of discontinuities. Our reconstruction method is also robust with respect to the smoothing(More)
Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human(More)
INBOUNDS is a real-time network based intrusion detection system being developed at Ohio University. INBOUNDS detects suspicious behavior by scrutinizing network information generated by TCPTrace [9] (a traffic analysis tool) and host data gathered by the monitors of DeSiDeRaTa [23-27] (dynamic, real-time resource management middleware). The use of these(More)