A Modified Edge-Based Region Growing Segmentation of Geometric Objects

With the gradual improvement of China’s railway net, the opening of international railways as well as the continuous growth of railway operating mileage, the workload of remeasuring railways is increasing. The traditional methods of remeasuring railways can not meet current high-speed and high-density operating conditions anymore in terms of safety, efficiency and quality, so a safer and more efficient measurement method is urgently needed.This thesis integrated various sensors on a self-mobile instrument, such as 3D laser scanner, digital image sensor and GNSS_IMU, designing a set of intelligent and integrated self-mobile scanning measurement system. This thesis proposed region growing segmentation based on the reflection intensity of point cloud. Through the secondary development of CAD, the menu for automatic processing of self-mobile scanning measurement system is designed to realize rail automatic segmentation, extraction of rail top points, fitting of plane parameters of railway line, calculation of curve elements and mileage management.The results show that self-mobile scanning measurement system overcomes the shortcomings of traditional railway measurement to some extent, and realizes intelligent measurement of railways.

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Region growing segmentation optimized by evolutionary approach and Maximum Entropy

Procedia Computer Science ◽

10.1016/j.procs.2019.04.148 ◽

2019 ◽

Vol 151 ◽

pp. 1046-1051 ◽

Cited By ~ 3

Author(s):

Mohammed Merzougui ◽

Ahmad El Allaoui

Keyword(s):

Maximum Entropy ◽

Region Growing ◽

Evolutionary Approach ◽

Region Growing Segmentation

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Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

International Journal of Image and Graphics ◽

10.1142/s0219467820500187 ◽

2020 ◽

Vol 20 (03) ◽

pp. 2050018

Author(s):

Neeraj Shrivastava ◽

Jyoti Bharti

Keyword(s):

Image Segmentation ◽

Region Growing ◽

Magnetic Resonance Images ◽

Medical Image Segmentation ◽

Seed Selection ◽

Seeded Region Growing ◽

Processing Strategies ◽

Edge Based ◽

Seed Points ◽

Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

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