Collage is a declarative programming model and runtime expressly targeted at building and deploying cross-organizational software as compositions of web components. Collage is based on an RDF data model, data-driven execution model, and flexible support for cross-organizational composition of both application and UI components. In this paper we outline a uniform set of Collage language features addressing end-to-end application design, including business objects, but with particular focus on user interaction, and adaptation to current interaction platforms such as web browsers.

Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.

article Free AccessVoiceXML for Web-based distributed conversational applications Author: Bruce Lucas IBM T.J. Watson Research Center, Yorktown Heights, NY IBM T.J. Watson Research Center, Yorktown Heights, NYView Profile Authors Info & Claims Communications of the ACMVolume 43Issue 901 September 2000pp 53–57https://doi.org/10.1145/348941.348985Published:01 September 2000Publication History 28citation1,431DownloadsMetricsTotal Citations28Total Downloads1,431Last 12 Months90Last 6 weeks8 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 SiteView all FormatsPDF

The architecture of the Data Explorer, a scientific visualization system, is described. Data Explorer supports the visualization of a wide variety of data by means of a flexible set of visualization modules. A single powerful data model common to all modules allows a wide range of data types to be imported and passed between modules. There is integral support for parallelism, affecting the data model and the execution model. The visualization modules are highly interoperable, due in part to the common data model, and exemplified by the renderer. An execution model facilitates parallelization of modules and incorporates optimizations such as caching. The two-process client-server system structure consists of a user interface that communicates with an executive via a dataflow language

This paper describes a flexible stereo verification system, STEREOSYS, and its application to the analysis of high resolution aerial photography. Stereo verification refers to the verification of hypotheses about a scene by stereo analysis of the scene. Unlike stereo interpretation, stereo verification requires only coarse indications of three-dimensional structure. In the case of aerial photography, this means coarse indications of the heights of objects above their surroundings. This requirement, together with requirements for robustness and for dense height measurements, shapes the decision about the stereo system to use. This paper discusses these design issues and describes an implementation.

Image matching refers to aligning two similar images related by a transformation such as a translation, rotation, etc. In its general form image matching is a problem of estimating the parameters that determine that transformation. These parameters may be a few global parameters or a field of parameters describing local transformations. This thesis explores in theory and by experiment image matching by the method of differences. The method uses intensity differences between the images together with the spatial intensity gradient to obtain from each image point a linear constraint on the match parameters; combining constraints from many points yields a parameter estimate. The method is particularly suitable where an initial estimate of the match parameters is available. In such cases it eliminates search which can be costly, particularly in multi-dimensional parameter spaces. Essential to the technique are smoothing, which increases the range of validity of the constraint provided by the gradient, and iteration, because the parameter estimate is an approximation. Smoothing increases the range of convergence but it decreases accuracy, so a coarse-fine approach is needed. A theoretical analysis supports these claims and provides a means for predicting the algorithm's behavior. The first application considered here, optical navigation, requires matching two images to determine the relative camera positions. Here the match parameters are the position parameters, because they determine the image transformation. In many cases, such as robot guidance, the required parameter estimate is available. Using information from points near edges minimizes error due to noise, specularity, etc. The relationship between the three-space geometry of the reference points and the stability of the algorithm is investigated. Optical navigation experiments using both real and synthetic images are presented. They support the claims of the theoretical analysis, and demonstrate a range of convergence and accuracy adequate for many tasks. The second application, stereo vision, is a problem of determining a field of local parameters, namely the distance values. Constraints from the neighborhood of each point contribute to its distance estimate. Experiments on both real and synthetic data provide encouraging results.

In this paper we present a type inference method for Prolog programs. The new idea is to describe a superset of the success set by associating a type substitution (an assignment of sets of ground terms to variables) with each head of definite clause. ...

Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.