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CLASSIC: consistent longitudinal alignment and segmentation for serial image computing

by Zhong Xue, Dinggang Shen, Christos Davatzikos - NeuroImage , 2006
"... Abstract. This paper proposes a temporally-consistent and spatiallyadaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm incorpora ..."
Abstract - Cited by 38 (15 self) - Add to MetaCart
Abstract. This paper proposes a temporally-consistent and spatiallyadaptive longitudinal MR brain image segmentation algorithm, referred to as CLASSIC, which aims at obtaining accurate measurements of rates of change of regional and global brain volumes from serial MR images. The algorithm

Globally Consistent Range Scan Alignment for Environment Mapping

by F. Lu, E. Milios - AUTONOMOUS ROBOTS , 1997
"... A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is alig ..."
Abstract - Cited by 531 (8 self) - Add to MetaCart
is aligned to a cumulative global model, and then merged to the model. Because different parts of the model are updated independently while there are errors in the registration, such an approachmay result in an inconsistent model. In this paper, we study the problem of consistent registration of multiple

Longitudinal data analysis using generalized linear models”.

by Kung-Yee Liang , Scott L Zeger - Biometrika, , 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
Abstract - Cited by 1526 (8 self) - Add to MetaCart
SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence

MAFFT version 5: improvement in accuracy of multiple sequence alignment

by Kazutaka Katoh, Kei-ichi Kuma, Hiroyuki Toh, Takashi Miyata - NUCLEIC ACIDS RES , 2005
"... The accuracy of multiple sequence alignment pro-gram MAFFT has been improved. The new version (5.3) of MAFFT offers new iterative refinement options, H-INS-i, F-INS-i and G-INS-i, in which pairwise alignment information are incorporated into objective function. These new options of MAFFT showed high ..."
Abstract - Cited by 801 (5 self) - Add to MetaCart
higher accuracy than currently available methods including TCoffee version 2 and CLUSTAL W in benchmark tests consisting of alignments of.50 sequences. Like the previously available options, the new options of MAFFT can handle hundreds of sequences on a standard desktop computer. We also examined

Pfam protein families database

by Robert D. Finn, John Tate, Jaina Mistry, Penny C. Coggill, Stephen John Sammut, Hans-rudolf Hotz, Goran Ceric, Kristoffer Forslund, Sean R. Eddy, Erik L. L. Sonnhammer, Alex Bateman - Nucleic Acids Research, 2008, 36(Database issue): D281–D288
"... Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI GenP ..."
Abstract - Cited by 771 (13 self) - Add to MetaCart
Pfam is a comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile hidden Markov models. The current release of Pfam (22.0) contains 9318 protein families. Pfam is now based not only on the UniProtKB sequence database, but also on NCBI Gen

A volumetric method for building complex models from range images,”

by Brian Curless , Marc Levoy - in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. ACM, , 1996
"... Abstract A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, ..."
Abstract - Cited by 1020 (17 self) - Add to MetaCart
Abstract A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction

Lucas-Kanade 20 Years On: A Unifying Framework: Part 3

by Simon Baker, Ralph Gross, Iain Matthews - International Journal of Computer Vision , 2002
"... Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision. Applications range from optical flow, tracking, and layered motion, to mosaic construction, medical image registration, and face coding. Numerous algorithms hav ..."
Abstract - Cited by 706 (30 self) - Add to MetaCart
have been proposed and a variety of extensions have been made to the original formulation. We present an overview of image alignment, describing most of the algorithms in a consistent framework. We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed. We

Active Appearance Models Revisited

by Iain Matthews, Simon Baker - International Journal of Computer Vision , 2003
"... Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM to ..."
Abstract - Cited by 462 (39 self) - Add to MetaCart
to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. We propose an efficient fitting algorithm for AAMs based on the inverse compositional image alignment algorithm. We show how the appearance variation can be "

PROBCONS: Probabilistic consistency-based multiple sequence alignment

by Chuong B. Do, Mahathi S. P. Mahabhashyam, Michael Brudno, Serafim Batzoglou - Genome Res , 2005
"... To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objec ..."
Abstract - Cited by 256 (10 self) - Add to MetaCart
objective functions for measuring alignment quality. In this paper, we introduce prob-abilistic consistency, a novel scoring function for multiple sequence comparisons. We present PROBCONS, a practical tool for progressive protein multiple sequence alignment based on prob-abilistic consistency, and evaluate

Fast and reliable prediction of noncoding RNAs

by Stefan Washietl, Ivo L. Hofacker, Peter F. Stadler - Proc Natl Acad Sci USA
"... We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scale-genomic screens. It consists of two basic components: ..."
Abstract - Cited by 335 (45 self) - Add to MetaCart
We report an efficient method to detect functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, yields excellent results already for a small number of aligned sequences and is suitable for large scale-genomic screens. It consists of two basic components
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