Correlating and Modeling of Extracted Features from PVT Images of Composites using Optical Flow Technique and Weight Elimination Algorithm Optimization [OFT-WEA]

Abstract

Abstract:A new approach to the use and implementation of Optical Flow technique is presented. The technique extracts features from presented images as a function of reference image and produces percentage of matching between the reference and tested images. The new approach in using Optical Flow lies in replacing the motion part of the algorithm with differential time related changes in an infrared thermal image sequence with frames of images taken as a result of applying the Pulse Video Thermography (PVT) technique. The sequence of images or frames is obtained for the tested structures of composites before and after impact damage. The resulted data of the tested images is used to establish mathematical model that can be used to predict impact energy from collected features or predict expected features from knowing impact damage level. To optimize the mathematical model, a new way of using Neural Networks is employed, which aims at obtaining a best fit for the used variables in the mathematical model, hence resulting in a better testing interpretation and more accurate prediction and classification of image features to improve future composite structures designs. The Neural Network Weigh Elimination Algorithm (WEA) is used and proved effective in predicting areas of damage.

Abnormalities in Ultrasonic (C-Scan) Images of Composite Structures: Impact Damaged Versus Hole Damaged

“…Thresholding selection techniques can be classified into two categories: bi-level and multi-level. A variety of thresholding approaches exist for image segmentation, ranging from conventional methods to intelligent techniques (Ma and Liu, 2016;Iskandarani, 2017;Suganya et al, 2013;Jung et al, 2017;Geng et al, 2015;Santhi and Wahida Banu, 2015). The interaction between the ultrasound wave and defects within the tested structure is a function of the defect size, defect orientation and propagation mode among other factors A material boundary constitutes a change in the acoustic impedance.…”

Data encoding and reconstruction of thermal imaging maps of impact damaged composite Structures using feature space and neural networks

“…Infrared image show defects, which through specific algorithms can be classified. In addition, the ability to obtain higher definition and contrast images will facilitate better analysis and computation [1][2][3][4][5][6][7][8][9][10][11][12][13][14] Artificial Neural network can map the critical properties of materials without the need for a computational mathematical model. Different researchers designed and applied neural network models and algorithms to characterize, and predict materials variables such as, strain and stress.…”

Effect of Correlating ImageThreshold Values with Image Gradient Field on Damage Detection in Composite Structures

“…Most of image segmentation techniques are based similarity and discontinuity. In addition to used segmentation methods, thresholding is regarded as a major contributor to achieve accurate image interpretation and subsequent classification, as histogram related thresholding is complex especially for multi-level thresholding [11][12][13][14][15]. It is critical for damage detection in composite structures to implement image enhancement techniques which is essential in image processing with particular consideration to intensity variation of image contents and its effect on segmentation.…”