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Spatially regularized estimation for the analysis
, 2012
"... Spatially regularized estimation for the analysis of DCE-MRI data ..."
Boosting with Spatial Regularization
"... By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifier ..."
Abstract
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By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base
Unsupervised Learning of Spatial Regularities
- http://citeseer.nj.nec.com/1418.html. [Online]. Available: http://citeseer.nj.nec.com/1418.html
, 1995
"... This paper examines the task of remote-sensing image analysis as an unsupervised learning task. Images are usually (very) large, and represent complex objects. Unsupervised learning, or clustering, may be of great help at several phases of the analysis. First, this paper describes a clustering algor ..."
Abstract
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Cited by 2 (0 self)
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algorithm. Then, the application of this algorithm to the segmentation phase is demonstrated. It is then argued that radiometry is insufficient to fully understand the scene in thematic terms. The next level of complexity is related to the incorporation of spatial information. This paper shows how this kind
On Detecting Spatial Regularity in Noisy Images
- Information Processing Letters
, 1999
"... Detecting spatial regularity in images arises in computer vision, scene analysis, military applications, and other areas. In this paper we present an O(n 5 2 ) algorithm that reports all maximal equally-spaced collinear subsets. The algorithm is robust in that it can tolerate noise or imprecision t ..."
Abstract
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Cited by 5 (0 self)
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Detecting spatial regularity in images arises in computer vision, scene analysis, military applications, and other areas. In this paper we present an O(n 5 2 ) algorithm that reports all maximal equally-spaced collinear subsets. The algorithm is robust in that it can tolerate noise or imprecision
Spatially regularized common spatial patterns for eeg classification
- in ICPR, 2010
"... In this paper, we propose a new algorithm for Brain-Computer Interface (BCI): the Spatially Regularized Common Spatial Patterns (SRCSP). SRCSP is an exten-sion of the famous CSP algorithm which includes spa-tial a priori in the learning process, by adding a regu-larization term which penalizes spati ..."
Abstract
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Cited by 5 (2 self)
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In this paper, we propose a new algorithm for Brain-Computer Interface (BCI): the Spatially Regularized Common Spatial Patterns (SRCSP). SRCSP is an exten-sion of the famous CSP algorithm which includes spa-tial a priori in the learning process, by adding a regu-larization term which penalizes
fMRI detection with spatial regularization
, 2005
"... Functional Magnetic Resonant Imaging (fMRI) is a non-invasive imaging technique used to study the brain. Neuroscientists have developed various algorithms to de-termine which voxels of the images are active. Most of these algorithms, operating on the signal of each voxel separately, are referred to ..."
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suggest that adjacent locations of the brain tend to be in the same activation state. We take advantage of these models and apply a Markov Random Field (MRF) spatial prior
MEDIAN FILTER WITH ABSOLUTE VALUE NORM SPATIAL REGULARIZATION
"... We provide a novel formulation for computing median filter with spatial regularization as minimizing a cost function composed of absolute value norms. We turn this cost minimization into an equivalent linear programming (LP) and solve its dual LP as a minimum cost flow (MCF) problem. The MCF is solv ..."
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We provide a novel formulation for computing median filter with spatial regularization as minimizing a cost function composed of absolute value norms. We turn this cost minimization into an equivalent linear programming (LP) and solve its dual LP as a minimum cost flow (MCF) problem. The MCF
Detecting and exploiting spatial regularity in data memory references
, 2003
"... The growing processor/memory performance gap causes the performance of many codes to be limited by memory accesses. If known to exist in an application, strided memory accesses forming streams can be targeted by optimizations such as prefetching, relocation, remapping, and vector loads. Undetected, ..."
Abstract
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Cited by 14 (5 self)
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to the subject; the concept of locality fails to capture the existence of streams in a program’s memory accesses. The contributions of this paper are as follows. First, we define spatial regularity as a means to discuss the presence and effects of streams. Second, we develop measures to quantify spatial
From spatial regularization to anatomical priors in fmri analysis
- INFORMATION PROCESSING IN MEDICAL IMAGING
, 2005
"... In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algorithms, creating a need for spatial regularization of the signal. Gaussian smoothing, traditionally employed to boost ..."
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Cited by 6 (0 self)
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In this paper, we study Markov Random Fields as spatial smoothing priors in fMRI detection. Relatively high noise in fMRI images presents a serious challenge for the detection algorithms, creating a need for spatial regularization of the signal. Gaussian smoothing, traditionally employed to boost
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