Graph Template Research Articles

Learning object models from graph templates

This paper describes a novel neural network (NN) based system for detecting rigid objects from their (2-D) gray-level image images. In this approach a labeled graph is employed to construct a template model for an object from the image region where the object is located. A novel network of NN (NoNN) is proposed to learn the examples of object model graph templates. The NoNN is composed of a set of subnetworks that are not connected to one another. The selected network topology improves the generalization of the classifier in terms of its Vapnik-Chervonenkis dimension (VCdim). Each subnetwork is a network of multilayer perceptron neural network classifiers operating in parallel with the rest of the system. Each subnetwork is assigned to learn the label of one vertex of a graph. The detection scheme combines the decisions of the subnetworks to classify an image graph extracted from an input image block. This visual computational model is potentially useful for partial matching where the object is occluded. Performance of the system is tested in modeling and detection of human eye regions in face images with some degree of variation in the direction of pose.

Graphical shape templates for automatic anatomy detection with applications to MRI brain scans.

A new method of model registration is proposed using graphical templates. A decomposable graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using robust relational local operators. A dynamic programming algorithm on the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination--local operators to describe points of interest/landmarks and a graph to describe their geometric arrangement in the plane--yields fast and precise matches of the model to the data with no initialization required. In addition, it provides a generic tool box for modeling shape in a variety of applications. This methodology is applied in the context of T2-weighted magnetic resonance (MR) axial and sagittal images of the brain to identify specific anatomies.

Graphical templates for model registration

A new method of model registration is proposed using graphical templates. A graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using local operators. A dynamic programming algorithm on decomposable subgraphs of the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination of local operators to describe points of interest/landmarks and a graph to describe their geometric orientation in the plane, yields fast and precise matches of the model to the data, with no initialization required.

Graph Templates: Graph Library for Sigmaplot for Windows. Volume I

The Bulletin of the Ecological Society of AmericaVolume 76, Issue 4 p. 235-235 Technological ToolFree Access Graph Templates: Graph Library for Sigmaplot for Windows. Volume I First published: 01 December 1995 https://doi.org/10.2307/20167981AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume76, Issue4December 1995Pages 235-235 RelatedInformation

On the universe of stable cellular neural networks

AbstractA cellular neural network (CNN) is a novel analogue circuit architecture with many desirable features. This paper extends previous stability results of CNNs to include classes of strictly sign‐symmetric and acyclic templates. We show that most of the 3×3 strictly sign‐symmetric templates are stable almost everywhere, with the unknown templates reduced to three classes. We also introduce template graphs and CNN graphs and utilize them to obtain results concerning stability and irreducibility of CNN templates.

A data-driven model for a subset of logic programming

There is a direct correspondence between semantic networks and a subset of logic programs, restricted only to binary predicates. The advantage of the latter is that it can describe not only the nodes and arcs comprising a semantic net, but also the data-retrieval operations applied to such nets. The main objective of this paper is to present a data-driven model of computation that permits this subset of logic programs to be executed on a highly parallel computer architecture. We demonstrate how logic programs may be converted into collections of data-flow graphs in which resolution is viewed as a process of finding matches between certain graph templates and portions of the data-flow graphs. This graph fitting process is carried out by messages propagating asynchronously through the data-flow graph; thus computation is entirely data driven, without the need for any centralized control and centralized memory. This permits a potentially large number of independent processing elements to cooperate in solving a given query.

Matching algorithms between a phonetic lattice and two types of templates - Lattice and graph.

We have developed two matching algorithms used in the speech recognition: (1) matching between two phonemic lattices, one for the input speech and the other for the template, and (2) matching between a phonemic lattice and a graph template. The algorithms are based on the dynamic programming. We introduced path weights proportional to the duration of lattice elements in both algorithms and loose constraints on accumulated durations in the graph-to-lattice matching. The algorithms works efficiently when the lattice elements are sorted with respect to the end times.