Search results for "Binarization"

showing 4 items of 4 documents

Yeast gene CMR1/YDL156W is consistently co-expressed with genes participating in DNA-metabolic processes in a variety of stringent clustering experim…

2013

© 2013 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited. The binarization of consensus partition matrices (Bi-CoPaM) method has, among its unique features, the ability to perform ensemble clustering over the same set of genes from multiple microarray datasets by using various clustering methods in order to generate tunable tight clusters. Therefore, we have used the Bi-CoPaM method to the most synchronized 500 cell-cycle-regulated yeast genes from different microarray datasets to produce four tight, specific …

Saccharomyces cerevisiae ProteinsCMR1/YDL156W1004Biomedical EngineeringBiophysicsG1/S transitionDNA repairBioengineeringDNA-Directed DNA PolymeraseSaccharomyces cerevisiaeBiologyDNA replication2244BiochemistryYeast geneBiomaterialschemistry.chemical_compoundReplication Protein Abinarization of consensus partition matrixCluster AnalysisCluster analysisGeneDNA-directed DNA polymeraseLicenseResearch Articlesta113GeneticsModels GeneticGene Expression ProfilingDNACreative commonsMicroarray AnalysisDNA-Binding ProteinsGenes cdcGene expression profilingchemistryDNABiotechnology
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Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery

2013

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight cluster…

Fuzzy clusteringMicroarraysSingle-linkage clusteringGenes FungalGene Expressionlcsh:MedicineBiologyFuzzy logicSet (abstract data type)Molecular GeneticsEngineeringGenome Analysis ToolsYeastsConsensus clusteringMolecular Cell BiologyDatabases GeneticCluster (physics)GeneticsCluster AnalysisBinarization of Consensus Partition Matrices (Bi-CoPaM)Cluster analysislcsh:ScienceGene clusteringBiologyOligonucleotide Array Sequence AnalysisGeneticsMultidisciplinarybusiness.industryCell Cycleta111lcsh:RComputational BiologyPattern recognitionGenomicsgene discoveryPartition (database)tunable binarization techniquesComputingMethodologies_PATTERNRECOGNITIONGenesCell cyclesSignal Processinglcsh:QArtificial intelligencebusinessGenomic Signal ProcessingAlgorithmsResearch Articleclustering
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Rethinking the sGLOH Descriptor

2018

sGLOH (shifting GLOH) is a histogram-based keypoint descriptor that can be associated to multiple quantized rotations of the keypoint patch without any recomputation. This property can be exploited to define the best distance between two descriptor vectors, thus avoiding computing the dominant orientation. In addition, sGLOH can reject incongruous correspondences by adding a global constraint on the rotations either as an a priori knowledge or based on the data. This paper thoroughly reconsiders sGLOH and improves it in terms of robustness, speed and descriptor dimension. The revised sGLOH embeds more quantized rotations, thus yielding more correct matches. A novel fast matching scheme is a…

Cascade matching0209 industrial biotechnologyHistogram binarizationRFDComputer scienceGLOHComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyCNN descriptorLIOP020901 industrial engineering & automationMROGHArtificial IntelligenceRobustness (computer science)Keypoint matchingSIFTHistogram0202 electrical engineering electronic engineering information engineeringSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabusiness.industryApplied MathematicsCognitive neuroscience of visual object recognitionPattern recognitionRotation invariant descriptorsGLOHMIOPComputational Theory and MathematicsKeypoint matching SIFT sGLOH RFDs LIOP MIOP MROGH CNN descriptors rotation invariant descriptors histogram binarization cascade matchingPrincipal component analysis020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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A stochastic shape and orientation model for fibres with an application to carbon nanotubes

2012

Methods are introduced for analysing the shape and orientation of planar fibres from greyscale images of fibrous systems. The sequence of image processing techniques needed for segmentation of fibres is described. The identified fibres were interpreted as deformed line segments for which two shape and two orientation parameters are estimated by the maximum likelihood method. The methods introduced are shown to perform quite well for simulated systems of deformed line segments with known properties. They were applied to TEM images of carbon nanotubes embedded in polycarbonate.

Materials scienceAcoustics and UltrasonicsMaterials Science (miscellaneous)General MathematicsCarbon nanotubesImage processingCarbon nanotube2D fibre identificationBinarizationGrayscaleDeformed line segmentslaw.inventionPlanarLine segmentlawRadiology Nuclear Medicine and imagingSegmentationPolycarbonateComposite materialInstrumentationlcsh:R5-920Orientation (computer vision)lcsh:MathematicsMultivariate von Mises distributionlcsh:QA1-939Computer Science::Computer Vision and Pattern Recognitionvisual_artSignal Processingvisual_art.visual_art_mediumComputer Vision and Pattern Recognitionlcsh:Medicine (General)BiotechnologyImage Analysis and Stereology
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