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MOORE, WARRELL AND PRINCE: HIERARCHIAL BOUNDARY PRIORS 1 Vistas: Hierarchial boundary priors using multiscale conditional random fields.

by Alastair P. Moore, Jonathan Warrell, Simon J. D. Prince
"... Boundary detection is a fundamental problem in computer vision. However, bound-ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am-biguity of the local signal. In ..."
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. In this paper we investigate incorporating a priori information into boundary detection. We learn a probabilistic model that describes a prior for object boundaries over small patches of the image. We then incorporate this boundary model into a mixture of multiscale conditional random fields, where the mixture

Vistas: Hierarchial boundary priors using multiscale conditional random fields.

by Jonathan Warrell, Alastair P. Moore, Simon J. D. Prince
"... Boundary detection is a fundamental problem in computer vision. However, bound-ary detection is difficult as it involves integrating multiple cues (intensity, color, texture) as well as trying to incorporate object class or scene level descriptions to mitigate the am-biguity of the local signal. In ..."
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. In this paper we investigate incorporating a priori information into boundary detection. We learn a probabilistic model that describes a prior for object boundaries over small patches of the image. We then incorporate this boundary model into a mixture of multiscale conditional random fields, where the mixture

BIRCH: an efficient data clustering method for very large databases

by Tian Zhang, Raghu Ramakrishnan, Miron Livny - In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD , 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem of ..."
Abstract - Cited by 576 (2 self) - Add to MetaCart
Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multi-dir nensional clataset. Prior work does not adequately address the problem

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
, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working

Statistical shape influence in geodesic active contours

by Michael E. Leventon, W. Eric, L. Grimson, Olivier Faugeras - In Proc. 2000 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hilton Head, SC , 2000
"... A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero l ..."
Abstract - Cited by 396 (4 self) - Add to MetaCart
level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior

Reinforcement learning with hierarchies of machines

by Ronald Parr, Stuart Russell - Advances in Neural Information Processing Systems 10 , 1998
"... We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can be transf ..."
Abstract - Cited by 285 (11 self) - Add to MetaCart
We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and provides a framework in which knowledge can

Global Economic Prospects and the Developing Countries 2000. Washington,D.C

by The International Bank , 1999
"... The findings, interpretations, and conclusions expressed here do not necessarily reflect the views of the Board of Executive Directors of the World Bank or the governments they represent. The World Bank cannot guarantee the accuracy of the data included in this work. The boundaries, colors, denomina ..."
Abstract - Cited by 368 (3 self) - Add to MetaCart
The findings, interpretations, and conclusions expressed here do not necessarily reflect the views of the Board of Executive Directors of the World Bank or the governments they represent. The World Bank cannot guarantee the accuracy of the data included in this work. The boundaries, colors

Learning and inferring transportation routines

by Lin Liao, Dieter Fox, Henry Kautz , 2004
"... This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation ..."
Abstract - Cited by 312 (22 self) - Add to MetaCart
or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any

Hierarchical topic models and the nested Chinese restaurant process

by David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum - Advances in Neural Information Processing Systems , 2004
"... We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested ..."
Abstract - Cited by 287 (32 self) - Add to MetaCart
We address the problem of learning topic hierarchies from data. The model selection problem in this domain is daunting—which of the large collection of possible trees to use? We take a Bayesian approach, generating an appropriate prior via a distribution on partitions that we refer to as the nested

Clustering by compression

by Rudi Cilibrasi, Paul M. B. Vitányi - IEEE Transactions on Information Theory , 2005
"... Abstract—We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the l ..."
Abstract - Cited by 297 (25 self) - Add to MetaCart
the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co
Results 1 - 10 of 3,478