Algorithm for automatic detection and measurement of Vickers indentation hardness using image processing

2020 ◽  
Vol 32 (1) ◽  
pp. 015407
Author(s):  
S M Domínguez-Nicolas ◽  
A L Herrera-May ◽  
L García-González ◽  
L Zamora-Peredo ◽  
J Hernández-Torres ◽  
...  
Keyword(s):  
Image Processing ◽  
Automatic Detection ◽  
Indentation Hardness ◽  
Vickers Indentation

Further analysis of the size effect in indentation hardness tests of some metals

1995 ◽  
Vol 10 (11) ◽  
pp. 2908-2915 ◽  
Author(s):  
M. Atkinson
Keyword(s):  
Size Effect ◽  
Strain Hardening ◽  
Indentation Size Effect ◽  
Plastic Hinge ◽  
Small Scale ◽  
Indentation Hardness ◽  
Vickers Indentation ◽  
Indentation Size ◽  
Hardness Tests ◽  
Indentation Tests

The variation of apparent hardness observed in previously reported Vickers indentation tests of metals is reexamined. Common deseriptions of the effect are shown to be inaccurate: the variation of apparent hardness is monotonic but not simple. The effect is consistent with varying size of a previously postulated “plastic hinge” at the perimeter of the indent. This complexity confers uncertainty on the estimation of characteristic macrohardness from small scale tests. Association of the indentation size effect with friction and with strain hardening is confirmed.

Image Processing Strategies for Automatic Detection of Common Gastroenterological Diseases

2018 ◽  
Author(s):  
Rafael Neujahr Copstein ◽  
Vicenzo Abichequer ◽  
Matheus Cruz Andrade ◽  
Lucas Almeida Machado ◽  
Evandro Rodrigues ◽  
...  
Keyword(s):  
Image Processing ◽  
Automatic Detection ◽  
Processing Strategies

An Approach for Automatic Detection and Grading of Macular Edema

2017 ◽  
pp. 1677-1702
Author(s):  
Jyoti Prakash Medhi
Keyword(s):  
Image Processing ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Blood Vessel ◽  
Macular Edema ◽  
Automatic Detection ◽  
Automatic Analysis ◽  
Fundus Images

Prolonged Diabetes causes massive destruction to the retina, known as Diabetic Retinopathy (DR) leading to blindness. The blindness due to DR may consequence from several factors such as Blood vessel (BV) leakage, new BV formation on retina. The effects become more threatening when abnormalities involves the macular region. Here automatic analysis of fundus images becomes important. This system checks for any abnormality and help ophthalmologists in decision making and to analyze more number of cases. The main objective of this chapter is to explore image processing tools for automatic detection and grading macular edema in fundus images.

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