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Results 1 - 10
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762
Policy gradient methods for reinforcement learning with function approximation.
- In NIPS,
, 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract
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Cited by 439 (20 self)
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is known to follow ∂ρ ∂θ in expected value Policy Gradient with Approximation Now consider the case in which Q π is approximated by a learned function approximator. If the approximation is sufficiently good, we might hope to use it in place of Q π in (2) and still point roughly in the direction
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
- INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2002
"... This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. This framework exploits boundary and region-based segmentation modules under a curve-based optimization objective function. The task of supervised texture segmentation is considered to demonst ..."
Abstract
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Cited by 312 (9 self)
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by unifying region and boundary-based information as an improved Geodesic Active Contour Model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the obtained PDE. According to this PDE, the curve propagation towards the final solution
2D/3D Registration Based on Volume Gradients
, 2005
"... We present a set of new methods for efficient and precise registration of any X-Ray modality (fluoroscopy, portal imaging or regular X-Ray imaging) to a CT data set. These methods require neither feature extraction nor 2D or 3D segmentation. Our main contribution is to directly perform the computati ..."
Abstract
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Cited by 4 (0 self)
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We present a set of new methods for efficient and precise registration of any X-Ray modality (fluoroscopy, portal imaging or regular X-Ray imaging) to a CT data set. These methods require neither feature extraction nor 2D or 3D segmentation. Our main contribution is to directly perform
New method of probability density estimation with application to mutual information based image registration
- In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR
, 2006
"... We present a new, robust and computationally efficient method for estimating the probability density of the intensity values in an image. Our approach makes use of a continuous representation of the image and develops a relation between probability density at a particular intensity value and image g ..."
Abstract
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Cited by 7 (1 self)
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We present a new, robust and computationally efficient method for estimating the probability density of the intensity values in an image. Our approach makes use of a continuous representation of the image and develops a relation between probability density at a particular intensity value and image
Gradient based optimization of an emst image registration function
- In Intl. Conf. Acoust., Speech Sig. Proc
, 2005
"... This paper examines the problem of registering images using an information theoretic metric (e.g., entropy) estimated using a Euclidean Minimum Spanning Tree (EMST). The objective is to find an extremum of the metric with respect to a vector of free parameters. One of the major difficulties posed by ..."
Abstract
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Cited by 4 (1 self)
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by such graph theoretic metrics is concurrently obtaining gradient information when the metric is computed. Obtaining the gradient is a first step in efficiently optimizing the metric. Our main contribution is to show how to obtain a gradient-based descent direction from the computation of the EMST metric. We
Fast Image-Based Localization using Direct 2D-to-3D Matching
- IN: IEEE 13TH INTERNATIONAL CONFERENCE ON COMPUTER VISION
, 2011
"... Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficientl ..."
Abstract
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Cited by 63 (8 self)
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to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof-the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods
Gradient intensitybased registration of multi-modal images of the brain
- in Proc. IEEE Int. Conf. Computer Vision (ICCV), Rio de Janeiro
, 2007
"... We present a fast and accurate framework for registra-tion of multi-modal volumetric images based on decoupled estimation of registration parameters utilizing spatial infor-mation in the form of ‘gradient intensity’. We introduce gra-dient intensity as a measure of spatial strength of an image in a ..."
Abstract
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Cited by 11 (5 self)
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given direction and show that it can be used to deter-mine the rotational misalignment independent of transla-tion between the images. The rotation parameters are ob-tained by maximizing the mutual information of 2D gradient intensity matrices obtained from 3D images, hence reduc-ing the dimensionality
Image registration in Hough space using gradient of images
- In Proc. Digital Image Computing: Techniques and Applications (DICTA
, 2007
"... We present an accurate and fast method for rigid reg-istration of images with large non-overlapping areas using a Hough transformation of image gradients. The Hough space representation of gradients can be used to separate estimation of the rotation parameter from the translation. It also allows us ..."
Abstract
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Cited by 2 (2 self)
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to estimate transformation parameters for 2D images over a 1D space, hence reducing the compu-tational complexity. The cost functions in the Hough do-main have larger capture ranges compared to the cost func-tions in the intensity domain. This allows the optimization to converge better in the presence
Coil sensitivity encoding for fast MRI. In:
- Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
Abstract
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Cited by 193 (3 self)
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is presented as well as an experimental in vitro evaluation and a selection of in vivo examples. THEORY AND METHODS In this section SENSE theory is presented and methods for image reconstruction from sensitivity encoded data are derived. The theory addresses the most general case of combining gradient
Fast Image Registration via Joint Gradient Maximization:
- Proceedings of SPIE Volume 6395 Electro-Optical and Infrared Systems: Technology and Applications III
, 2006
"... We present a computationally inexpensive method for multi-modal image registration. Our approach employs a joint gradient similarity function that is applied only to a set high spatial gradient pixels. We obtain motion parameters by maximizing the similarity function by gradient ascent method, which ..."
Abstract
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We present a computationally inexpensive method for multi-modal image registration. Our approach employs a joint gradient similarity function that is applied only to a set high spatial gradient pixels. We obtain motion parameters by maximizing the similarity function by gradient ascent method
Results 1 - 10
of
762
