Non contact Two Dimensional Strain Measurement Based on Improved Sub Image Search Algorithm using Correlation Technique

Shifani, S. Agnes; Premi, M. S. Godwin

doi:10.1109/icssit46314.2019.8987916

Cited by 1 publication

(1 citation statement)

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“…References [ 3 , 4 ] indicate the assessment of metal disfigurement that is utilized in the automobile industry. Here, the exactness depends on the subset size, spot design [ 5 ], correlation model, shape work, subpixel insertion plan, and subpixel enlistment calculation [ 6 , 7 ], and the Newton-Raphson iterative method, semi-Newton procedure, and point-based count have furthermore been proposed to quicken the streamlining circle. In continuous examples, iterative spatial area cross-association procedure is one of the most extensively used subpixel selection estimations, yet the NR method was developed completely by diminishing its computation multifaceted nature.…”

Section: Introductionmentioning

confidence: 99%

Accurate Measurement of Strain in Noncontact Surface Deformation Using Subset-Based Digital Image Correlation

Shifani

Premi

2021

Contrast Media & Molecular Imaging

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The measurement of strain using some contact techniques has some drawbacks like less accuracy and it takes larger computation time for finding each location of subpixels. Thus, a faster noncontact Digital Image Correlation (DIC) mechanism is utilized along with the traditional techniques to measure the strain. The Newton-Raphson (NR) technique is considered to be an accepted mechanism for accurate tracking of different intensity relocation. Generally, the issue regarding the DIC mechanism is its computational cost. In this paper, an interpolation technique is utilized to accomplish a high precision rate and faster image correlation; thereby it reduces the computation time required for finding the matched pixel and viably handles the rehashing relationship process. Hence, the proposed mechanism provides better efficiency along with a reduced number of iterations required for finding the identity. The number of iterations can be reduced using the Sum of Square of Subset Intensity Gradients (SSSIG) method. The evaluation of the projected scheme is tested with different images through various parameters. Finally, the outcome indicates that the projected mechanism takes only a few milliseconds to match the best matching location, whereas the prevailing techniques require 16 seconds for the same operation with the same step size. This demonstrates the effectiveness of the proposed scheme.

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