Topic
Adjacency list
About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.
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TL;DR: The regeneration of existing legacy floorplans (corresponding to the user defined dimensions) has been demonstrated, with an additional feature to construct symmetric dimensioned solutions.
23 citations
TL;DR: A new algorithm based on the relative position constraint (RPC) between regions for region matching in stereoscopic images, illustrated by a set of synthesized and real stereo images, with a comparison to the well-known algorithmbased on the adjacency constraint.
23 citations
TL;DR: A methodology for the automatic development of flat patterns for any given folded structure based on enumerating all possible spanning trees of the face adjacency graph (the graph that represents the connectivity among the faces of the structure) since any spanning tree represents a potential topological unfolding of that structure.
Abstract: There are applications in the sheet metal, paperboard, packaging and various other industries for the computer-aided design of flat patterns for folding into some desired 3D folded structure made of piecewise flat faces. This paper describes a methodology for the automatic development of flat patterns for any given folded structure based on enumerating all possible spanning trees of the face adjacency graph (the graph that represents the connectivity among the faces of the structure) since any spanning tree represents a potential topological unfolding of that structure. Complications found in non-manifold structures, such as where more than two faces are joined at one common edge, are also addressed in this work by way of recognizing topologically invalid spanning trees. Furthermore, a strategy is also developed to detect overlapping of faces within the pattern purely by checking its topology defined in the spanning tree, without first having to geometrically construct the pattern layout. Finally, three measures of compactness are adopted as the optimality criteria for the methodology to output flat pattern results ranked according to their compactness. The procedure is implemented as a computer program and applied to six example structures in this paper to illustrate the capabilities of the methodology.
23 citations
TL;DR: A method which can recognize form features and reconstruct 3D part from 2D CAD data automatically is proposed, and a new structure of form feature adjacency graph (FFAG) is devised to record the related attibutes of each form feature.
23 citations
17 Sep 2010
TL;DR: A novel label propagation algorithm based on nonnegative sparse representation (NSR) for bioinformatics and biometrics is presented and extensive experimental results demonstrate that label propagation algorithms based on NSR outperforms the standardlabel propagation algorithm.
Abstract: Graph-based semi-supervised learning strategy plays an important role in the semi-supervised learning area. This paper presents a novel label propagation algorithm based on nonnegative sparse representation (NSR) for bioinformatics and biometrics. Firstly, we construct a sparse probability graph (SPG) whose nonnegative weight coefficients are derived by nonnegative sparse representation algorithm. The weights of SPG naturally reveal the clustering relationship of labeled and unlabeled samples; meanwhile automatically select appropriate adjacency structure as compared to traditional semi-supervised learning algorithm. Then the labels of unlabeled samples are propagated until algorithm converges. Extensive experimental results on biometrics, UCI machine learning and TDT2 text datasets demonstrate that label propagation algorithm based on NSR outperforms the standard label propagation algorithm.
23 citations