Graph-cut methods for grain boundary segmentation

Wang, Song; Waggoner, Jarrell; Simmons, Jeff

doi:10.1007/s11837-011-0111-5

Cited by 7 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interactive image segmentation is an important tool in bio-medical imaging for the accurate delineation of complex 3-dimensional objects such as organs and bones [21], identifying synaptic pathways [22], as well as identifying, counting and characterizing different cell types [23]. Similar applications are also found in material science [24], geology, manufacturing and food inspection.…”

Section: Humans Sketch and Machine Learning Does The Restmentioning

confidence: 99%

Interactive Machine Learning in Data Exploitation

2013

View full text Add to dashboard Cite

The goal of interactive machine learning is to help scientists and engineers exploit more of their specialized data in less time. Interactive machine learning focuses on methods that empower domain experts to control and direct machine learning tools from within the deployed environment, whereas traditional machine learning does this in the development environment. This difference allows interactive machine learning systems to be more responsive, more accurate and cheaper to develop and maintain. This article provides a basic introduction to the main components and tries to untangle the many ideas that must be combined to produce practical interactive learning systems. It also describes recent developments in machine learning that have significantly advanced the theoretical and practical foundations for the next generation of interactive tools.

show abstract

Section: Humans Sketch and Machine Learning Does The Restmentioning

confidence: 99%

Interactive Machine Learning in Data Exploitation

2013

View full text Add to dashboard Cite

show abstract

“…This has been largely owed to recent advances in automated data collection [59] , advancing segmentation techniques [60][61][62][63][64][65] and further integration of three-dimensional data within simulation and modeling domains [66] . Approaches for acquiring three-dimensional data exist on varying length scales and are nearly as numerous as the amounts of materials systems investigated as illustrated by the abbreviated list provided in the previous section.…”

Section: Current State Of the Art Three-dimensional Interrogation Metmentioning

confidence: 99%

Porosity in millimeter-scale welds of stainless steel : three-dimensional characterization.

Aagesen

Madison²

2012

View full text Add to dashboard Cite

A variety of edge joints utilizing a continuous wave Nd:YAG laser have been produced and examined in a 304-L stainless steel to advance fundamental understanding of the linkage between processing and resultant microstructure in high-rate solidification events. Acquisition of three-dimensional reconstructions via micro-computed tomography combined with traditional metallography has allowed for qualitative and quantitative characterization of weld joints in a material system of wide use and broad applicability. The presence, variability and distribution of porosity, has been examined for average values, spatial distributions and morphology and then related back to fundamental processing parameters such as weld speed, weld power and laser focal length.4

show abstract

“…In addition, it has been found experimentally that a significant reduction in the grain size is associated with a lower material durability and damage resistance [14,15]. Therefore, an accurate and automatic method for tracking the collective microstructural changes that occur as a result of various external force impacts can substantially facilitate microstructural analysis and modeling, thus shortening the time to the development of new materials and leading to the better operation and maintenance of such materials during their service life [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Dharmawan

Lee

2022

Applied Sciences

View full text Add to dashboard Cite

Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs’ deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering.

show abstract

Graph-cut methods for grain boundary segmentation

Cited by 7 publications

References 11 publications

Interactive Machine Learning in Data Exploitation

Interactive Machine Learning in Data Exploitation

Porosity in millimeter-scale welds of stainless steel : three-dimensional characterization.

Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Contact Info

Product

Resources

About