Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
19,274
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multi-band Image Segmentation
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1996
"... We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum and c ..."
Abstract
-
Cited by 774 (20 self)
- Add to MetaCart
We present a novel statistical and variational approach to image segmentation based on a new algorithm named region competition. This algorithm is derived by minimizing a generalized Bayes/MDL criterion using the variational principle. The algorithm is guaranteed to converge to a local minimum
Public-key cryptosystems based on composite degree residuosity classes
- IN ADVANCES IN CRYPTOLOGY — EUROCRYPT 1999
, 1999
"... This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes: a trapdoor permutation and two homomorphic probabilist ..."
Abstract
-
Cited by 1009 (4 self)
- Add to MetaCart
This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes: a trapdoor permutation and two homomorphic
A new learning algorithm for blind signal separation
-
, 1996
"... A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in-formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of ..."
Abstract
-
Cited by 622 (80 self)
- Add to MetaCart
A new on-line learning algorithm which minimizes a statistical de-pendency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual in-formation (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number
Recognition-by-components: A theory of human image understanding
- Psychological Review
, 1987
"... The perceptual recognition of objects is conceptualized to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components, such as blocks, cylinders, wedges, and cones. The fundamental assumption of the proposed theory, recog ..."
Abstract
-
Cited by 1272 (23 self)
- Add to MetaCart
, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N ^ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermmation. The detection
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
- In Proceedings of the 20th International Joint Conference on Artificial Intelligence
, 2007
"... Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from Wikipedi ..."
Abstract
-
Cited by 562 (9 self)
- Add to MetaCart
Computing semantic relatedness of natural language texts requires access to vast amounts of common-sense and domain-specific world knowledge. We propose Explicit Semantic Analysis (ESA), a novel method that represents the meaning of texts in a high-dimensional space of concepts derived from
Probabilistic Latent Semantic Analysis
- In Proc. of Uncertainty in Artificial Intelligence, UAI’99
, 1999
"... Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Sema ..."
Abstract
-
Cited by 771 (9 self)
- Add to MetaCart
Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent
Matlab user’s guide
, 2005
"... This product or document is protected by copyright and distributed under licenses restricting its use, copying, distribution, and decompilation. No part of this product or document may be reproduced in any form by any means without prior written authorization of Sun and its licensors, if any. Portio ..."
Abstract
-
Cited by 535 (0 self)
- Add to MetaCart
. Portions of this product may be derived from the UNIX ® system, licensed from Novell, Inc., and from the Berkeley 4.3 BSD system, licensed from the University of California. UNIX is a registered trademark in the United States and other countries and is exclusively licensed by X/Open Company Ltd. Third
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
- IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limi ..."
Abstract
-
Cited by 639 (15 self)
- Add to MetaCart
-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
An affine invariant interest point detector
- In Proceedings of the 7th European Conference on Computer Vision
, 2002
"... Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape of ..."
Abstract
-
Cited by 1467 (55 self)
- Add to MetaCart
Abstract. This paper presents a novel approach for detecting affine invariant interest points. Our method can deal with significant affine transformations including large scale changes. Such transformations introduce significant changes in the point location as well as in the scale and the shape
CATH -- a hierarchic classification of protein domain structures
- STRUCTURE
, 1997
"... Background: Protein evolution gives rise to families of structurally related proteins, within which sequence identities can be extremely low. As a result, structure-based classifications can be effective at identifying unanticipated relationships in known structures and in optimal cases function can ..."
Abstract
-
Cited by 470 (33 self)
- Add to MetaCart
can also be assigned. The ever increasing number of known protein structures is too large to classify all proteins manually, therefore, automatic methods are needed for fast evaluation of protein structures. Results: We present a semi-automatic procedure for deriving a novel hierarchical
Results 1 - 10
of
19,274
