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Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms
 IEEE Transactions on Image Processing
, 1993
"... Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked " by a (binary) image J contained in I. This transformation can be ext ..."
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Cited by 336 (3 self)
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Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of a binary image I (the mask) which are \marked " by a (binary) image J contained in I. This transformation can
Morphological grayscale reconstruction: Definition, efficient algorithms and applications in image analysis
 IN IEEE INT. COMPUTER VISION AND PATTERN RECOG. CONFERENCE
, 1992
"... Reconstruction is part of a set of morphological image transformations [4] referred to as geodesic [1]. In the binary case, it simply extracts the connected components of a binary image I which are “marked” by an image J contained in I. Reconstruction can be extended to the grayscale case, and the f ..."
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Cited by 7 (2 self)
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Reconstruction is part of a set of morphological image transformations [4] referred to as geodesic [1]. In the binary case, it simply extracts the connected components of a binary image I which are “marked” by an image J contained in I. Reconstruction can be extended to the grayscale case
Threedimensional object recognition from single twodimensional images
 Artificial Intelligence
, 1987
"... A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single grayscale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottomup from the visual input. Instead, ..."
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Cited by 484 (7 self)
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A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single grayscale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottomup from the visual input. Instead
Improved Localization of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach
 J. Cogn. Neurosci
, 1993
"... We describe a comprehensive linear approach to the prob lem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole st ..."
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Cited by 263 (19 self)
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is recursively fiood4illed ,o determine the topology of the gray.white matter border, and (3) the resulting continuous surface is refinc by relaxing it against the original 3D grayscale image using a deformable template method, which is also used to computationally flatten the cortex for k'asier vic
New EdgeDirected Interpolation
 IEEE Transactions on Image Processing
, 2001
"... This paper proposes an edgedirected interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a lowresolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality betw ..."
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Cited by 244 (2 self)
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interpolation and covariancebased adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results
IFTWatershed from GrayScale Marker
, 2002
"... The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we intro ..."
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Cited by 17 (11 self)
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The watershed transform and the morphological reconstruction are two of the most important operators for image segmentation in the framework of mathematical morphology. In many situations, the segmentation requires the classical watershed transform of a reconstructed image. In this paper, we
A morphological reconstruction algorithm for separating offterrain points from terrain points in laser scanning data
 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 36 (3/W19
, 2005
"... During the last decade various techniques have been proposed to extract the ground surface from airborne LIDAR data. The basic problem is the separation of terrain points from offterrain points which are both recorded by the LIDAR sensor. In particular geometry driven filtering, detection or classi ..."
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Cited by 16 (4 self)
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to separate 3D offterrain points from the terrain points. Morphological grayscale reconstruction plays the key role in the proposed algorithm to produce the bare ground. After a short description of morphological reconstruction an algorithm based on this technique is presented. Issues of the implementation
Grayscale Area Opening and Closing, their efficient implementation and applications
 in Proc. Eurasip Workshop on Mathematical Morphology and its Applications to Signal Processing
, 1993
"... The filter that removes from a binary image its connected components with area smaller than a parameter is called area opening. From a morphological perspective, this filter is an algebraic opening, and it can be extended to grayscale images. The properties of area openings and their dual area cl ..."
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Cited by 63 (0 self)
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The filter that removes from a binary image its connected components with area smaller than a parameter is called area opening. From a morphological perspective, this filter is an algebraic opening, and it can be extended to grayscale images. The properties of area openings and their dual area
Distance transforms of sampled functions
 Cornell Computing and Information Science
, 2004
"... This paper provides lineartime algorithms for solving a class of minimization problems involving a cost function with both local and spatial terms. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary ..."
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Cited by 175 (9 self)
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by an arbitrary sampled function. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A useful consequence of our techniques is a simple, fast method for computing the Euclidean distance transform of a binary image
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