The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform
Diethard Tautz - One of the best experts on this subject based on the ideXlab platform.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background: In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction. Results: Evaluation was performed on micro-CT images of rodent skulls for which two independent sets of manual landmark annotations had been performed. This allowed assessment of landmark accuracy in terms of both the distance between manual and automatic annotations, and the repeatability of manual and automatic annotation. Automatic annotation attained accuracies equivalent to those achievable through manual annotation by an expert for 87.5% of the points, with significantly higher repeatability. Conclusions: Whilst user input was required to produce the training data and in a final error correction stage, the software was capable of reducing the number of manual annotations required in a typical landmark identification processusing3Ddatabya factorof ten,potentiallyallowingmuchlargerdatasetstobeannotatedandthusincreasing the statistical power of the results from subsequent processing e.g. Procrustes/principal component analysis. The software is freely available, under the GNU General Public Licence, from our web-site (www.tina-vision.net).
Paul A Bromiley - One of the best experts on this subject based on the ideXlab platform.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background: In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction. Results: Evaluation was performed on micro-CT images of rodent skulls for which two independent sets of manual landmark annotations had been performed. This allowed assessment of landmark accuracy in terms of both the distance between manual and automatic annotations, and the repeatability of manual and automatic annotation. Automatic annotation attained accuracies equivalent to those achievable through manual annotation by an expert for 87.5% of the points, with significantly higher repeatability. Conclusions: Whilst user input was required to produce the training data and in a final error correction stage, the software was capable of reducing the number of manual annotations required in a typical landmark identification processusing3Ddatabya factorof ten,potentiallyallowingmuchlargerdatasetstobeannotatedandthusincreasing the statistical power of the results from subsequent processing e.g. Procrustes/principal component analysis. The software is freely available, under the GNU General Public Licence, from our web-site (www.tina-vision.net).
Jiong Yang - One of the best experts on this subject based on the ideXlab platform.
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efficient classification across Multiple Database relations a crossmine approach
IEEE Transactions on Knowledge and Data Engineering, 2006Co-Authors: Xiaoxin Yin, Jiawei Han, Jiong YangAbstract:Relational Databases are the most popular repository for structured data, and is thus one of the richest sources of knowledge in the world. In a relational Database, Multiple relations are linked together via entity-relationship links. Multirelational classification is the procedure of building a classifier based on information stored in Multiple relations and making predictions with it. Existing approaches of inductive logic programming (recently, also known as relational mining) have proven effective with high accuracy in multirelational classification. Unfortunately, most of them suffer from scalability problems with regard to the number of relations in Databases. In this paper, we propose a new approach, called CrossMine, which includes a set of novel and powerful methods for multirelational classification, including 1) tuple ID propagation, an efficient and flexible method for virtually joining relations, which enables convenient search among different relations, 2) new definitions for predicates and decision-tree nodes, which involve aggregated information to provide essential statistics for classification, and 3) a selective sampling method for improving scalability with regard to the number of tuples. Based on these techniques, we propose two scalable and accurate methods for multirelational classification: CrossMine-Rule, a rule-based method and CrossMine-Tree, a decision-tree-based method. Our comprehensive experiments on both real and synthetic data sets demonstrate the high scalability and accuracy of the CrossMine approach
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crossmine efficient classification across Multiple Database relations
Lecture Notes in Computer Science, 2004Co-Authors: Xiaoxin Yin, Jiawei Han, Jiong YangAbstract:Most of today's structured data is stored in relational data- bases. Such a Database consists of Multiple relations that are linked together conceptually via entity-relationship links in the design of relational Database schemas. Multi-relational classification can be widely used in many disciplines including financial decision making and medical research. However, most classification approaches only work on single “flat” data relations. It is usually difficult to convert Multiple relations into a single flat relation without either introducing huge “universal relation” or losing essential information. Previous works using Inductive Logic Programming approaches (recently also known as Relational Mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they fail to achieve high scalability w.r.t. the number of relations in Databases because they repeatedly join different relations to search for good literals. In this paper we propose CrossMine, an efficient and scalable approach for multi-relational classification. CrossMine employs tuple ID propagation, a novel method for virtually joining relations, which enables flexible and efficient search among Multiple relations. CrossMine also uses aggregated information to provide essential statistics for classification. A selective sampling method is used to achieve high scalability w.r.t. the number of tuples in the Databases. Our comprehensive experiments on both real and synthetic Databases demonstrate the high scalability and accuracy of CrossMine.
Camila Campos Mantello - One of the best experts on this subject based on the ideXlab platform.
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deep expression analysis reveals distinct cold response strategies in rubber tree hevea brasiliensis
BMC Genomics, 2019Co-Authors: Camila Campos Mantello, Lucas Boatwright, Carla Cristina Da Silva, Erivaldo Jose Scaloppi, Paulo De Souza Goncalves, Brad W Barbazuk, Anete Pereira De SouzaAbstract:Natural rubber, an indispensable commodity used in approximately 40,000 products, is fundamental to the tire industry. The rubber tree species Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell-Arg., which is native the Amazon rainforest, is the major producer of latex worldwide. Rubber tree breeding is time consuming, expensive and requires large field areas. Thus, genetic studies could optimize field evaluations, thereby reducing the time and area required for these experiments. In this work, transcriptome sequencing was used to identify a full set of transcripts and to evaluate the gene expression involved in the different cold-response strategies of the RRIM600 (cold-resistant) and GT1 (cold-tolerant) genotypes. We built a comprehensive transcriptome using Multiple Database sources, which resulted in 104,738 transcripts clustered in 49,304 genes. The RNA-seq data from the leaf tissues sampled at four different times for each genotype were used to perform a gene-level expression analysis. Differentially expressed genes (DEGs) were identified through pairwise comparisons between the two genotypes for each time series of cold treatments. DEG annotation revealed that RRIM600 and GT1 exhibit different chilling tolerance strategies. To cope with cold stress, the RRIM600 clone upregulates genes promoting stomata closure, photosynthesis inhibition and a more efficient reactive oxygen species (ROS) scavenging system. The transcriptome was also searched for putative molecular markers (single nucleotide polymorphisms (SNPs) and microsatellites) in each genotype. and a total of 27,111 microsatellites and 202,949 (GT1) and 156,395 (RRIM600) SNPs were identified in GT1 and RRIM600. Furthermore, a search for alternative splicing (AS) events identified a total of 20,279 events. The elucidation of genes involved in different chilling tolerance strategies associated with molecular markers and information regarding AS events provides a powerful tool for further genetic and genomic analyses of rubber tree breeding.
Neil A Thacker - One of the best experts on this subject based on the ideXlab platform.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction.
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semi automatic landmark point annotation for geometric morphometrics
Frontiers in Zoology, 2014Co-Authors: Paul A Bromiley, Anja C Schunke, Hossein Ragheb, Neil A Thacker, Diethard TautzAbstract:Background: In previous work, the authors described a software package for the digitisation of 3D landmarks for use in geometric morphometrics. In this paper, we describe extensions to this software that allow semi-automatic localisation of 3D landmarks, given a Database of manually annotated training images. Multi-stage registration was applied to align image patches from the Database to a query image, and the results from Multiple Database images were combined using an array-based voting scheme. The software automatically highlights points that have been located with low confidence, allowing manual correction. Results: Evaluation was performed on micro-CT images of rodent skulls for which two independent sets of manual landmark annotations had been performed. This allowed assessment of landmark accuracy in terms of both the distance between manual and automatic annotations, and the repeatability of manual and automatic annotation. Automatic annotation attained accuracies equivalent to those achievable through manual annotation by an expert for 87.5% of the points, with significantly higher repeatability. Conclusions: Whilst user input was required to produce the training data and in a final error correction stage, the software was capable of reducing the number of manual annotations required in a typical landmark identification processusing3Ddatabya factorof ten,potentiallyallowingmuchlargerdatasetstobeannotatedandthusincreasing the statistical power of the results from subsequent processing e.g. Procrustes/principal component analysis. The software is freely available, under the GNU General Public Licence, from our web-site (www.tina-vision.net).