Estimation of limestone particle mass from 2D images

Banta, Larry E.; Cheng, Kai Yuan; Zaniewski, John P

doi:10.1016/s0032-5910(03)00061-5

Cited by 34 publications

(14 citation statements)

References 3 publications

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“…Volume of particles can be determined from 2-D images with some assumptions. Some researchers used area of particles directly measured from 2-D images to evaluate gradation curves [14] while others used mass determined from volume of particles with some assumptions [10] - [11]. The assumptions in determining volume from 2-D images would result in some errors.…”

Section: Resultsmentioning

confidence: 99%

“…The assumptions in determining volume from 2-D images would result in some errors. Also, if mass of individual particles are measured manually, that would be a time consuming process [11]. In this research, volume of particles was determined using the results of image analysis and measurements of gravel particles obtained using a Vanier caliper.…”

Section: Resultsmentioning

confidence: 99%

“…However, 2-D images cannot measure volume of particles directly. Banta et al (2003) [11] studied particle gradation curves by 2-D images of 4.75-25mm size limestone and found that image analysis gave good results. However, mass of individual particles were measured using a balance to compare gradation curves by the two methods.…”

Section: Introductionmentioning

confidence: 99%

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Image Analysis Techniques on Evaluation of Particle Size Distribution of Gravel

Kumara¹,

Hayano²,

Ogiwara³

2012

geomate

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Particle size distribution of granular materials is usually evaluated by sieve analysis test. In this research, an image analysis technique using ImageJ is proposed to evaluate particle size distribution of gravels. On particular conditions, some differences of gradation curves determined by sieve analysis and image analysis were observed. Based on the results, several aspects related to image analyzing are discussed in the paper. They include appropriate evaluation of particle grain size in image analysis, minimization of shadow effects appeared in images, effects of number of particles adopted for sieve analysis and image analysis and so on. It was found that grain size in image analysis should be defined appropriately to compare the gradation curves by the two methods. Probably, due to light effects, it was also observed that black color sheets are better than white color sheets to place particles. This method can be used as an in-situ test method since this method needs only a camera and a computer.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Image Analysis Techniques on Evaluation of Particle Size Distribution of Gravel

Kumara¹,

Hayano²,

Ogiwara³

2012

geomate

View full text Add to dashboard Cite

show abstract

“…A regression approach similar to ours but with different descriptors was used to estimate limestone particle masses in the construction industry (Banta et al, 2003). Chaarani et al (2013) report a correlation between volume and area measurements for brain ventricles but do not attempt a regression.…”

Section: Introductionmentioning

confidence: 99%

Volume Estimation From Single Images: An Application to Pancreatic Islets

et al. 2018

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The present paper deals with the problem of volume estimation of individual objects from a single 2D view. Our main application is volume estimation of pancreatic (Langerhans) islets and the single 2D view constraint comes from the time and equipment limitations of the standard clinical procedure.Two main approaches are followed in this paper. First, two regression-based methods are proposed, using a set of simple shape descriptors of the segmented image of the islet. Second, two example-based methods are proposed, based on a database of islets with known volume. For training and evaluation, islet volumes were determined by OPT microscopy and a semi-automatical stereological volume estimation using the so-called Fakir probes.The performance of the single image volume estimation methods is studied on a set of 99 islets from human donors. Further experiments were also performed on a stone dataset and on synthetic 3D shapes, generated using a flexible stochastic particle model. The proposed methods are fast and the experimental results show that in most situations the proposed methods perform significantly better than the methods currently used in clinical practice, which are based on simple spherical or ellipsoidal models.

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“…Watershed approach is believed to be efficient and adequate for components segmentation for gray scale or binary images. Vincent [2] provides a revised algorithm of this kind whose basic principle is that 1 The method can be described as follows: the initial gray-scaled image is first converted to the corresponding binary counterpart using the technique briefed in Banta's work [3,4] . To the obtained binary image, the morphological erosion is applied to convert it to the corresponding gray-scale image characterized with 3-D topographical features.…”

Section: Introductionmentioning

confidence: 99%

Saddle-point based separation of touched objects in 2-D image

Chen

Banta

Jiang

2006

J. of Electron.(China)

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In many image analysis and processing problems, discriminating the size and shape of each individual object in an aggregate pile projected in an image is an important practice. It is relatively easy to distinguish these features among the objects already separated from each other. The problems will be undoubtedly more complex and of greater challenge if the objects are touched or/and overlapped. This letter presents an algorithm that can be used to separate the touches and overlaps existing in the objects within a 2-D image. The approach is first to convert the gray-scale image to its corresponding binary one and then to the 3-D topographic one using the erosion operations. A template (or mask) is engineered to search the topographic surface for the saddle point, from which the segmenting orientation is determined followed by the desired separating operation. The algorithm is tested on a real image and the running result is adequately satisfying and encouraging.

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Estimation of limestone particle mass from 2D images

Cited by 34 publications

References 3 publications

Image Analysis Techniques on Evaluation of Particle Size Distribution of Gravel

Image Analysis Techniques on Evaluation of Particle Size Distribution of Gravel

Volume Estimation From Single Images: An Application to Pancreatic Islets

Saddle-point based separation of touched objects in 2-D image

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