Results 1 - 10 of 2,585

Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images.

by Stuart Geman , Donald Geman - IEEE Trans. Pattern Anal. Mach. Intell. , 1984
"... Abstract-We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs di ..."
Abstract - Cited by 5126 (1 self) - Add to MetaCart
system isolates low energy states ("annealing"), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result

Image registration methods: a survey.

by Barbara Zitová , Jan Flusser , 2003
"... Abstract This paper aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrical ..."
Abstract - Cited by 760 (10 self) - Add to MetaCart
geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (areabased and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image

Multi-Modal Volume Registration by Maximization of Mutual Information

by William M. Wells, III, Paul Viola, Ron Kikinis , 1996
"... A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, ..."
Abstract - Cited by 458 (23 self) - Add to MetaCart
, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with raw images; no preprocessing or feature detection is required. As opposed to feature-based

Robust mapping and localization in indoor environments using sonar data

by Juan D. Tardós, José Neira, Paul M. Newman, John J. Leonard - INT. J. ROBOTICS RESEARCH , 2002
"... In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, su ..."
Abstract - Cited by 179 (30 self) - Add to MetaCart
In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features

A New Point Matching Algorithm for Non-Rigid Registration

by Haili Chui, Anand Rangarajan , 2002
"... Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features. I ..."
Abstract - Cited by 356 (3 self) - Add to MetaCart
Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features

Data Association in Stochastic Mapping Using the Joint Compatibility Test

by Jose Neira, Juan D. Tards , 2001
"... In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact that measurement ..."
Abstract - Cited by 252 (15 self) - Add to MetaCart
In this paper, we address the problem of robust data association for simultaneous vehicle localization and map building. We show that the classical gated nearest neighbor approach, which considers each matching between sensor observations and features independently, ignores the fact

Feature-Based Methods For Large Scale Dynamic Programming

by John N. Tsitsiklis, Benjamin Van Roy - Machine Learning , 1994
"... We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be Combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations, that ..."
Abstract - Cited by 178 (8 self) - Add to MetaCart
We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be Combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations

Visualizing Data using t-SNE

by Laurens van der Maaten, Geoffrey Hinton , 2008
"... We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly b ..."
Abstract - Cited by 280 (13 self) - Add to MetaCart
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly

Autonomous Feature-Based Exploration

by P. Newman, M. Bosse, J. Leonard , 2003
"... This paper presents an algorithm for feature-based exploration of a priori unknown environments. We aim to build a robot that, unsupervised, plans its motion such that it continually increases both the spatial extent and detail of its world model - its map. We present a method by which the planned m ..."
Abstract - Cited by 17 (4 self) - Add to MetaCart
This paper presents an algorithm for feature-based exploration of a priori unknown environments. We aim to build a robot that, unsupervised, plans its motion such that it continually increases both the spatial extent and detail of its world model - its map. We present a method by which the planned

Self Organization of a Massive Document Collection

by Teuvo Kohonen, Samuel Kaski, Krista Lagus, Jarkko Salojarvi, Vesa Paatero, Antti Saarela - IEEE Transactions on Neural Networks
"... This article describes the implementation of a system that is able to organize vast document collections according to textual similarities. It is based on the Self-Organizing Map (SOM) algorithm. As the feature vectors for the documents we use statistical representations of their vocabularies. The m ..."
Abstract - Cited by 264 (15 self) - Add to MetaCart
. The main goal in our work has been to scale up the SOM algorithm to be able to deal with large amounts of high-dimensional data. In a practical experiment we mapped 6,840,568 patent abstracts onto a 1,002,240-node SOM. As the feature vectors we used 500-dimensional vectors of stochastic figures obtained
Results 1 - 10 of 2,585