We have developed a CS1 curriculum that uses a robotics context to teach introductory programming [1]. Core to our approach is that each student has their own personal robot. Our robot and software have been specifically developed to support the needs of a CS1 curriculum. We frame traditional problems (robot control) in terms that are personal, relevant, and fun. Initial trial classes have shown that our approach is successful and adaptable.

In the established procedural model of information visualization, the first operation is to transform raw data into data tables [1]. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision.

In this paper, we present a high speed optical motion capture method that can measure three dimensional motion, orientation, and incident illumination at tagged points in a scene. We use tracking tags that work in natural lighting conditions and can be imperceptibly embedded in attire or other objects. Our system supports an unlimited number of tags in a scene, with each tag uniquely identified to eliminate marker reacquisition issues. Our tags also provide incident illumination data which can be used to match scene lighting when inserting synthetic elements. The technique is therefore ideal for on-set motion capture or real-time broadcasting of virtual sets. Unlike previous methods that employ high speed cameras or scanning lasers, we capture the scene appearance using the simplest possible optical devices - a light-emitting diode (LED) with a passive binary mask used as the transmitter and a photosensor used as the receiver. We strategically place a set of optical transmitters to spatio-temporally encode the volume of interest. Photosensors attached to scene points demultiplex the coded optical signals from multiple transmitters, allowing us to compute not only receiver location and orientation but also their incident illumination and the reflectance of the surfaces to which the photosensors are attached. We use our untethered tag system, called Prakash, to demonstrate methods of adding special effects to captured videos that cannot be accomplished using pure vision techniques that rely on camera images.

A major problem with interactive displays based on front projection is that users cast undesirable shadows on the display surface. This paper demonstrates that shadows can be muted by redundantly illuminating the display surface using multiple projectors, all mounted at different locations. However, this technique alone does not eliminate shadows: Multiple projectors create multiple dark regions on the surface (penumbral occlusions) and cast undesirable light onto the users. These problems can be solved by eliminating shadows and suppressing the light that falls on occluding users by actively modifying the projected output. This paper categorizes various methods that can be used to achieve redundant illumination, shadow elimination, and blinding light suppression and evaluates their performance.

Front projection allows large displays to be deployed relatively easily. However, it is sometimes difficult to find a location to place a projector, especially for ad-hoc installations. Additionally, front projection suffers from shadows and occlusions, making it ill-suited for interactive displays. The GVU-PROCAMS system allows programmers to deploy projectors and displays easily in arbitrary locations by enabling enhanced keystone correction via warping on 3D hardware. In addition, it handles the calibration of multiple projectors using computer vision to produce a redundantly illuminated surface. Redundant illumination offers robustness in the face of occlusions, providing a user with the experience of a rear-projected surface. This paper presents a stand-alone application (WinPVRP) and a programming system (GVU-PROCAMS) that easily allows others to create projected displays with enhanced warping and redundant illumination.

Video projectors have typically been used to display images on surfaces whose geometric relationship to the projector remains constant, such as walls or pre-calibrated surfaces. In this paper, we present a technique for projecting content onto moveable surfaces that adapts to the motion and location of the surface to simulate an active display. This is accomplished using a projector based location tracking techinque. We use light sensors embedded into the moveable surface and project low-perceptibility Gray-coded patterns to first discover the sensor locations, and then incrementally track them at interactive rates. We describe how to reduce the perceptibility of tracking patterns, achieve interactive tracking rates, use motion modeling to improve tracking performance, and respond to sensor occlusions. A group of tracked sensors can define quadrangles for simulating moveable displays while single sensors can be used as control inputs. By unifying the tracking and display technology into a single mechanism, we can substantially reduce the cost and complexity of implementing applications that combine motion tracking and projected imagery.

There has been little research on end-user program development beyond the activity of programming. Devising ways to address additional activities related to end-user program development may be critical, however, because research shows that a large proportion of the programs written by end users contain faults. Toward this end, we have been working on ways to provide formal "software engineering" methodologies to end-user programmers. This paper describes an approach we have developed for supporting assertions in end-user software, focusing on the spreadsheet paradigm. We also report the results of a controlled experiment, with 59 end-user subjects, to investigate the usefulness of this approach. Our results show that the end users were able to use the assertions to reason about their spreadsheets, and that doing so was tied to both greater correctness and greater efficiency.

article Appendices A--D: A scalable method for deductive generalization in the spreadsheet paradigm Share on Authors: Margaret Burnett Oregon State University Oregon State UniversityView Profile , Sherry Yang Oregon Institute of Technology Oregon Institute of TechnologyView Profile , Jay Summet Oregon State University Oregon State UniversityView Profile Authors Info & Claims ACM Transactions on Computer-Human InteractionVolume 9Issue 4December 2002 pp 1–5https://doi.org/10.1145/586081.586082Published:01 December 2002 0citation420DownloadsMetricsTotal Citations0Total Downloads420Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper, we present an efficient method for automatically generalizing programs written in spreadsheet languages. The strategy is to do generalization through incremental analysis of logical relationships among concrete program entities from the perspective of a particular computational goal. The method uses deductive dataflow analysis with algebraic back-substitution rather than inference with heuristics, and there is no need for generalization-related dialog with the user. We present the algorithms and their time complexities and show that, because the algorithms perform their analyses incrementally, on only the on-screen program elements rather than on the entire program, the method is scalable. Performance data is presented to help demonstrate the scalability.