Distance Transform Algorithms And Their Implementation And Evaluation

2007 ◽  
pp. 33-60 ◽  
Author(s):  
George J. Grevera
Keyword(s):  
Distance Transform

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

A Hybrid Approach for the Lung(s) Nodule Detection using the Deformable Model and Distance Transform

2020 ◽  
pp. -1--1
Author(s):  
Ayyaz Hussain ◽  
, Mohammed Alawairdhi ◽  
Fayez Alazemi ◽  
Sajid Khan ◽  
Muhammad Ramzan
Keyword(s):  
Hybrid Approach ◽  
Deformable Model ◽  
Distance Transform ◽  
Nodule Detection

3‐D texture characterized by accessibility measurements, based on the grey weighted distance transform

Bioimaging ◽  
1994 ◽  
Vol 2 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Karel C Strasters ◽  
Arnold W M Smeulders ◽  
Hans T M van der Voort
Keyword(s):  
Distance Transform ◽  
Weighted Distance

scholarly journals Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1365
Author(s):  
Tao Zheng ◽  
Zhizhao Duan ◽  
Jin Wang ◽  
Guodong Lu ◽  
Shengjie Li ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed ◽  
High Performance ◽  
High Efficiency ◽  
Semantic Segmentation ◽  
Raspberry Pi ◽  
Distance Transform ◽  
Testing Stage ◽  
Sampling Points ◽  
Information Sampling

Semantic segmentation of room maps is an essential issue in mobile robots’ execution of tasks. In this work, a new approach to obtain the semantic labels of 2D lidar room maps by combining distance transform watershed-based pre-segmentation and a skillfully designed neural network lidar information sampling classification is proposed. In order to label the room maps with high efficiency, high precision and high speed, we have designed a low-power and high-performance method, which can be deployed on low computing power Raspberry Pi devices. In the training stage, a lidar is simulated to collect the lidar detection line maps of each point in the manually labelled map, and then we use these line maps and the corresponding labels to train the designed neural network. In the testing stage, the new map is first pre-segmented into simple cells with the distance transformation watershed method, then we classify the lidar detection line maps with the trained neural network. The optimized areas of sparse sampling points are proposed by using the result of distance transform generated in the pre-segmentation process to prevent the sampling points selected in the boundary regions from influencing the results of semantic labeling. A prototype mobile robot was developed to verify the proposed method, the feasibility, validity, robustness and high efficiency were verified by a series of tests. The proposed method achieved higher scores in its recall, precision. Specifically, the mean recall is 0.965, and mean precision is 0.943.

Retinal image blood vessel extraction and quantification with Euclidean distance transform approach

2020 ◽  
Author(s):  
Kuryati Kipli ◽  
Mohammed Enamul Hoque ◽  
Lik Thai Lim ◽  
Tengku Mohd Afendi Zulcaffle ◽  
Siti Kudnie Sahari ◽  
...  
Keyword(s):  
Blood Vessel ◽  
Retinal Image ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform ◽  
Vessel Extraction

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

2019 ◽  
Vol 34 (11) ◽  
pp. 972-990 ◽  
Author(s):  
Hao Pu ◽  
Taoran Song ◽  
Paul Schonfeld ◽  
Wei Li ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Distance Transform

scholarly journals Use of the Distance Transform for Integration of Local Measurements: Principle and Application in Chemical Engineering

2016 ◽  
Vol 22 (2) ◽  
pp. 422-431 ◽  
Author(s):  
Loïc Sorbier ◽  
Frédéric Bazer-Bachi ◽  
Yannick Blouët ◽  
Maxime Moreaud ◽  
Virginie Moizan-Basle
Keyword(s):  
Chemical Engineering ◽  
Least Square ◽  
Infinite Cylinder ◽  
Distance Transform ◽  
High Symmetry ◽  
Object A ◽  
Local Measurements ◽  
Symmetrical Object ◽  
Relative Errors ◽  
The Cost

AbstractWe propose an original methodology to integrate local measurement for nontrivial object shape. The method employs the distance transform of the object and least-square fitting of numerically computed weighting functions extracted from it. The method is exemplified in the field of chemical engineering by calculating the global metal concentration in catalyst grains from uneven metal distribution profiles. Applying the methodology on synthetic profiles with the help of a very simple deposition model allows us to evaluate the accuracy of the method. For high symmetry objects such as an infinite cylinder, relative errors on global concentration are lower than 1% for well-resolved profiles. For a less symmetrical object, a tetralobe, the best estimator gives a relative error below 5% at the cost of increased measurement time. Applicability on a real case is demonstrated on an aged hydrodemetallation catalyst. Sampling of catalyst grains at the inlet and outlet of the reactor allowed conclusions concerning different reactivity for the trapped metals.

scholarly journals Tumor Spheres Quantification with Smoothed Euclidean Distance Transform

2018 ◽  
Vol 08 (01) ◽  
Author(s):  
Ismet Sahin ◽  
Yu Zhang ◽  
Florencia McAllister
Keyword(s):  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform
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