• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 200
Next 10 →

Geodesic Active Contours

by Vicent Caselles, Ron Kimmel, Guillermo Sapiro , 1997
"... A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both in ..."
Abstract - Cited by 1425 (47 self) - Add to MetaCart
A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both

Noname manuscript No. (will be inserted by the editor) Interleaving Distance between Merge Trees

by Dmitriy Morozov, Kenes Beketayev, Gunther H. Weber
"... the date of receipt and acceptance should be inserted later Abstract Merge trees are topological descriptors of scalar functions. They record how the subsets of the domain where the function value does not exceed a given threshold are connected. We define a distance between merge trees, called an in ..."
Abstract - Add to MetaCart
the date of receipt and acceptance should be inserted later Abstract Merge trees are topological descriptors of scalar functions. They record how the subsets of the domain where the function value does not exceed a given threshold are connected. We define a distance between merge trees, called

Near neighbor search in large metric spaces

by Sergey Brin - In Proceedings of the 21th International Conference on Very Large Data Bases , 1995
"... Given user data, one often wants to find approximate matches in a large database. A good example of such a task is finding images similar to a given image in a large collection of images. We focus on the important and technically difficult case where each data element is high dimensional, or more ge ..."
Abstract - Cited by 216 (0 self) - Add to MetaCart
generally, is represented by a point in a large metric spaceand distance calculations are computationally expensive. In this paper we introduce a data structure to solve this problem called a GNAT- Geometric Near-neighbor Access Tree. It is based on the philosophy that the data structure should act as a

Measuring the Distance between Merge Trees

by Kenes Beketayev, Damir Yeliussizov, Dmitriy Morozov, Gunther H. Weber, Bernd Hamann, Kenes Beketayev, Damir Yeliussizov, Dmitriy Morozov, Gunther H. Weber, Bernd Hamann
"... Abstract Merge trees represent the topology of scalar functions. To assess the topo-logical similarity of functions, one can compare their merge trees. To do so, one needs a notion of a distance between merge trees, which we define. We provide examples of using our merge tree distance and compare th ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Abstract Merge trees represent the topology of scalar functions. To assess the topo-logical similarity of functions, one can compare their merge trees. To do so, one needs a notion of a distance between merge trees, which we define. We provide examples of using our merge tree distance and compare

Language trees and zipping.

by Dario Benedetto , Emanuele Caglioti , Vittorio Loreto - PRL, , 2002
"... In this Letter we present a very general method for extracting information from a generic string of characters, e.g., a text, a DNA sequence, or a time series. Based on data-compression techniques, its key point is the computation of a suitable measure of the remoteness of two bodies of knowledge. ..."
Abstract - Cited by 103 (0 self) - Add to MetaCart
. On the other hand, other systems are intrinsically described by a string of characters, e.g., DNA and protein sequences, language. When analyzing a string of characters the main question is to extract the information it brings. For a DNA sequence this would correspond to the identification of the subsequences

Algorithms for merged indexes

by Goetz Graefe - BTW Conf , 2007
"... Merged indexes are B-trees that contain multiple traditional indexes and interleave their records based on a common sort order. In relational databases, merged indexes implement “master-detail clustering ” of related records, e.g., orders and order details. Thus, merged indexes shift de-normalizatio ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
Merged indexes are B-trees that contain multiple traditional indexes and interleave their records based on a common sort order. In relational databases, merged indexes implement “master-detail clustering ” of related records, e.g., orders and order details. Thus, merged indexes shift de

Interleaved S+P Pyramidal Decomposition with Refined Prediction Model

by Marie Babel, Olivier Déforges, Joseph Ronsin - In ICIP , 2005
"... Scalability and others functionalities such as the Region of Interest encoding become essential properties of an efficient image coding scheme. Within the framework of lossless compression techniques, S+P and CALIC represent the state-of-the-art. The proposed Interleaved S+P algorithm outperforms th ..."
Abstract - Cited by 26 (19 self) - Add to MetaCart
. The image coding is done in two main steps, so that the first one supplies a LAR lowresolution image of good visual quality, and the second one allows a lossless reconstruction. The method exploits an implicit context modelling, intrinsic property of our content-based quad-tree like representation. 1.

Sequence Cluster Merging using Normalized Phylogenetic Tree Distances Towards partial requirement of L529

by Arvind Gopu , 2004
"... In this report, we explain and illustrate a novel and simple method to merge sequence cluster fragments. The method uses normalized phylogenetic tree dis-tances as its base. We intend to have this method as one of the various methods that will be included in a cluster merging framework that is under ..."
Abstract - Add to MetaCart
In this report, we explain and illustrate a novel and simple method to merge sequence cluster fragments. The method uses normalized phylogenetic tree dis-tances as its base. We intend to have this method as one of the various methods that will be included in a cluster merging framework

Dependency formation and directionality of tree construction

by Norvin Richards - MIT Working Papers in Linguistics 34 , 1999
"... Cyclicity effects are implemented in the Minimalist program, in part, by interleaving tree-building operations with other syntactic operations. The order of operations in the derivation is linked to the hierarchical structure of the tree, since this structure is created in the course of the derivati ..."
Abstract - Cited by 22 (0 self) - Add to MetaCart
Cyclicity effects are implemented in the Minimalist program, in part, by interleaving tree-building operations with other syntactic operations. The order of operations in the derivation is linked to the hierarchical structure of the tree, since this structure is created in the course

Bayesian hierarchical clustering

by Katherine A. Heller, Zoubin Ghahramani - In Proceedings of the 22nd International Conference on Machine Learning. ACM , 2005
"... We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which ..."
Abstract - Cited by 72 (11 self) - Add to MetaCart
can be used to compute the predictive distribution of a test point and the probability of it belonging to any of the existing clusters in the tree. (2) It uses a model-based criterion to decide on merging clusters rather than an ad-hoc distance metric. (3) Bayesian hypothesis testing is used to decide
Next 10 →
Results 1 - 10 of 200
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University