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Regularized, fast, and robust analytical Q‐ball imaging
We propose a regularized, fast, and robust analytical solution for the Q‐ball imaging (QBI) reconstruction of the orientation distribution function (ODF) together with its detailed validation and aExpand
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Vector-valued image regularization with PDEs: a common framework for different applications
TLDR
In this paper, we focus on techniques for vector-valued image regularization, based on variational methods and PDE. Expand
  • 653
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A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry
TLDR
A robust approach to image matching by exploiting the only available geometric constraint, namely, the epipolar constraint. Expand
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Using Canny's criteria to derive a recursively implemented optimal edge detector
  • R. Deriche
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 1 June 1987
TLDR
A highly efficient recursive algorithm for edge detection is presented. Expand
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A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape
TLDR
We present a survey of a class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Expand
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Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
TLDR
This paper presents a new variational framework for detecting and tracking multiple moving objects in image sequences using boundary-based information. Expand
  • 1,162
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Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions
TLDR
We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). Expand
  • 577
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Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
  • N. Paragios, R. Deriche
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 11 February 2002
TLDR
This paper presents a novel variational framework to deal with frame partition problems in Computer Vision. Expand
  • 898
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Vector-valued image regularization with PDE's: a common framework for different applications
TLDR
We address the problem of vector-valued image regularization with variational methods and PDEs. Expand
  • 273
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A variational framework for active and adaptative segmentation of vector valued images
Much effort has been made in integrating different information in a variational framework to segment images. Recent works on curve propagation were able to incorporate stochastic information (seeExpand
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