3-D chamfer distances and norms in anisotropic grids

Fouard, Céline; Malandain, Grégoire

doi:10.1016/j.imavis.2004.06.009

Cited by 30 publications

(36 citation statements)

References 23 publications

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“…Using Farey sets, Fouard and Malandain (2005) computed optimal integer approximations for anisotropic voxels. These estimators were found by minimizing the maximum error, without taking the RMS error and mean error into account.…”

Section: Discussionmentioning

confidence: 99%

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Measurement of the Shortest Path Length; Distance Estimation Within the 3d Borders of a Tissue of Interest

Boer¹,

Voorbraak

Berg³

2011

Image Anal Stereol

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Volume data, such as 3D reconstructions from histological sections or MRI and CT data, are commonly used in studies in biology and medicine. The quantification of morphological parameters and changes within a region of interest is a key concern in such studies. Specifically, it is often required to measure the distance between two points. These distance measurements have to follow a track through the tissue when measuring in sheetlike or contorted organs like the developing heart. A tool was developed that enables this kind of distance measurements. Three existing neighborhood estimators were compared; two of Verwer and one of Kiryati, all originally designed to compute chamfer distances in data sets with isotropic, cubic voxels. The estimators were therefore adjusted to handle non-isotropic data sets. Moreover, the shortest path along a track within a given tissue was calculated. The measurement of known distances, through a simplified model of an early heart tube, with anisotropic voxels was used decide which of the three estimators should be implemented. The observed Root Mean Square (RMS) errors were similar to the ones reported in literature in the unrestrained isotropic case. The adjusted Verwer estimator measuring in a 5 3 neighborhood performed best by far with the lowest mean and RMS errors.

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Section: Discussionmentioning

confidence: 99%

“…The 2D and 3D algorithms are mostly designed to handle data sets with isotropic, cubic, voxels. Handling of nonisotropic datasets is described by Coquin and Bolon (1995) for pixels and Sintorn and Borgefors (2004) and Fouard and Malandain (2005) for voxels.…”

Section: Introductionmentioning

confidence: 99%

Measurement of the Shortest Path Length; Distance Estimation Within the 3d Borders of a Tissue of Interest

Boer¹,

Voorbraak

Berg³

2011

Image Anal Stereol

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“…This Euclidean Distance Transformation converts a binary image to another image of the same size, such that each pixel has a value equal to the exact Euclidean distance to the nearest object pixel, where most other algorithms approximate the true Euclidean distance 6,7 . The new image is called the exact Euclidean Distance Map (E 2 DM) of the old image.…”

Section: The Calculation Of Distances Is Based On the Fast Exact Euclmentioning

confidence: 99%

Video surveillance using distance maps

2006

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Human vigilance is limited; hence, automatic motion and distance detection is one of the central issues in video surveillance. Hereby, many aspects are of importance, this paper specially addresses: efficiency, achieving real-time performance, accuracy, and robustness against various noise factors. To obtain fully controlled test environments, an artificial development center for robot navigation is introduced in which several parameters can be set (e.g., number of objects, trajectories and type and amount of noise). In the videos, for each following frame, movement of stationary objects is detected and pixels of moving objects are located from which moving objects are identified in a robust way. An Exact Euclidean Distance Map (E 2 DM) is utilized to determine accurately the distances between moving and stationary objects. Together with the determined distances between moving objects and the detected movement of stationary objects, this provides the input for detecting unwanted situations in the scene. Further, each intelligent object (e.g., a robot), is provided with its E 2 DM, allowing the object to plan its course of action. Timing results are specified for each program block of the processing chain for 20 different setups. So, the current paper presents extensive, experimentally controlled research on real-time, accurate, and robust motion detection for video surveillance, using E 2 DMs, which makes it a unique approach.

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“…A few methods have been developed to find and correct the wrong pixels, obtaining the exact ED 8 . Such semi-exact ED transforms have also been developed for 3D images 9,10 . Several other (parallel) implementations of ED transforms have been proposed 2,11 .…”

Section: Borgeforsmentioning

confidence: 99%

Three-dimensional fast exact Euclidean distance (3D-FEED) maps

Schouten

Kuppens

Broek

2006

Vision Geometry XIV

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In image and video analysis, distance maps are frequently used. They provide the (Euclidean) distance (ED) of background pixels to the nearest object pixel. Recently, the Fast Exact Euclidean Distance (FEED) transformation was launched. In this paper, we present the three dimensional (3D) version of FEED. 3D-FEED is compared with four other methods for a wide range of 3D test images. 3D-FEED proved to be twice as fast as the fastest algorithm available. Moreover, it provides true exact EDs, where other algorithms only approximate the ED. This unique algorithm makes the difference, especially there where time and precision are of importance.

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3-D chamfer distances and norms in anisotropic grids

Cited by 30 publications

References 23 publications

Measurement of the Shortest Path Length; Distance Estimation Within the 3d Borders of a Tissue of Interest

Measurement of the Shortest Path Length; Distance Estimation Within the 3d Borders of a Tissue of Interest

Video surveillance using distance maps

Three-dimensional fast exact Euclidean distance (3D-FEED) maps

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