Distance functions on digital pictures

Rosenfeld, Azriel; Pfaltz, John L.

doi:10.1016/0031-3203(68)90013-7

Cited by 753 publications

(316 citation statements)

References 1 publication

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“…However, a good approximation to d(x) can be computed very rapidly for all x on a fine rectangular grid by using the distance transform algorithm developed in image processing (Rosenfeld and Pfalz, 1968). This also works in higher dimensions (Borgefors, 1984(Borgefors, , 1986.…”

Section: 3 Empty Space Function Fmentioning

confidence: 99%

Analysis of a Three-Dimensional Point Pattern with Replication

Baddeley¹,

Moyeed²,

Howard³

et al. 1993

Applied Statistics

112

158

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SUMMARYTechniques for analysing three-dimensional spatial point patterns are demonstrated on data from a confocal microscope recording the locations of cells in three dimensions. New computational techniques are proposed for edge corrections and empty space measurement. A novel feature of the data is replication and nesting in a sampling design: multiple spatial patterns were observed from each of several animals. For this we develop a ratio regression approach.

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Section: 3 Empty Space Function Fmentioning

confidence: 99%

Analysis of a Three-Dimensional Point Pattern with Replication

Baddeley¹,

Moyeed²,

Howard³

et al. 1993

Applied Statistics

112

158

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“…From a binary image made of an object O and its background O , a distance transformation [1] makes an image, the distance map, in which the value of any pixel is the distance from this pixel to the object O, i.e. the distance to the nearest pixel of O,…”

Section: Introductionmentioning

confidence: 99%

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Cuisenaire

Macq

1999

Computer Vision and Image Understanding

127

108

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We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent than all other DTs recently proposed. Contrary to all other propagation DTs, it appears to remain o(n 2 ) even in the worst-case scenario.

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“…Distance transforms (Rosenfeld and Pfaltz, 1968;Borgefors, 1986) have a long history in the image processing domain and can be used for a wide range of applications (Jones et al, 2006), including the creation of object skeletons and the efficient application of morphological operations. A 2D distance transform converts a binary image to a gray level image, where each pixel has a value corresponding to the distance to the nearest feature pixel.…”

Section: Distance Transformsmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

349

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Distance functions on digital pictures

Cited by 753 publications

References 1 publication

Analysis of a Three-Dimensional Point Pattern with Replication

Analysis of a Three-Dimensional Point Pattern with Replication

Fast Euclidean Distance Transformation by Propagation Using Multiple Neighborhoods

Medical image processing on the GPU – Past, present and future

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