scholarly journals Development of MATLAB GUI Based on Blob Analysis for Automatic Extraction of Defects

2021 ◽  
Author(s):  
Kang Hong ◽  
Lihua Yuan ◽  
Zhe Li
Keyword(s):  
Time History ◽  
Region Of Interest ◽  
General Information ◽  
Automatic Extraction ◽  
Sequence Diagrams ◽  
Detection Technology ◽  
Quantitative Extraction ◽  
Blob Analysis ◽  
Defect Extraction ◽  
Gray Values

Abstract This study introduces a graphical user interface (GUI) based on MATLAB to realize the automatic ex-traction of sizes of defects from the infrared sequence. To obtain the edge of the defect at deeper layer, a fusion stratagem of the maximum of gray values is adopted for an image subset in the sequence. Blob analysis to the fusion image is used to obtain the general information of defects of a specimen including the distributions and numbers of defects. The frame image is determined for a certain defect according to the peak of the time history curve of sensitive region variance. It can yield a region of interest (ROI) to expand the blob in the selected frame and the defect can be acquired by image segmentation. The results show that through this GUI, a better thermal image can be selected from a set of infrared sequence diagrams for quantitative extraction of different buried depth defect areas, which realizes automatic defect extraction, and its relative error is within 5%. The research on infrared automatic detection technology has certain significance.

scholarly journals Automatic Extraction of Material Defect Size by Infrared Image Sequence

2020 ◽  
Vol 10 (22) ◽  
pp. 8248
Author(s):  
Lihua Yuan ◽  
Xiao Zhu ◽  
Quanbin Sun ◽  
Haibo Liu ◽  
Peter Yuen ◽  
...  
Keyword(s):  
Infrared Image ◽  
Region Of Interest ◽  
Image Sequence ◽  
General Information ◽  
Fiber Reinforced Polymer ◽  
Key Frame ◽  
Reinforced Polymer ◽  
Blob Analysis ◽  
Different Depths ◽  
Infrared Image Sequence

A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

scholarly journals Scan–rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics

2019 ◽  
Vol 62 (4) ◽  
pp. 483-494
Author(s):  
Christina Andica ◽  
Koji Kamagata ◽  
Takuya Hayashi ◽  
Akifumi Hagiwara ◽  
Wataru Uchida ◽  
...  
Keyword(s):  
Gray Matter ◽  
Healthy Subjects ◽  
Volume Fraction ◽  
Diffusion Tensor ◽  
Intraclass Correlation ◽  
Region Of Interest ◽  
Brain Regions ◽  
Automatic Extraction ◽  
Dispersion Index ◽  
Diffusion Tensor Imaging Dti

Abstract Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.

Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction

2017 ◽  
Vol 17 (04) ◽  
pp. 1750024 ◽  
Author(s):  
Qianwen Li ◽  
Zhihua Wei ◽  
Cairong Zhao
Keyword(s):  
Image Analysis ◽  
Computational Complexity ◽  
Clustering Algorithm ◽  
Region Of Interest ◽  
Region Growing ◽  
Regions Of Interest ◽  
Automatic Extraction ◽  
Seeded Region Growing ◽  
Roi Extraction ◽  
Ap Clustering

Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.

scholarly journals Evaluation of Failure Probability in Series System of Three-Axle Trucks under Strong Crosswind

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yahui Hu ◽  
Yingshi Guo ◽  
Rui Fu ◽  
Qingjin Xu
Keyword(s):  
Failure Probability ◽  
Lateral Displacement ◽  
Center Of Mass ◽  
Time History ◽  
General Information ◽  
Time Varying ◽  
Series System ◽  
Road Transportation ◽  
Force Coefficient ◽  
Average Wind Speed

The probability of wind-induced failure accidents in three-axle trucks under pulsating strong crosswinds and the corresponding critical safe speed are investigated in this study. Reliability theory and random fuzzy methods are utilized to establish the membership function of the failure probability in the series system (FPSS) composed of rollover, side-slip, and rotation failure accidents. The Kaman spectrum is used to realistically simulate the fluctuating wind time history curves of different average speeds. Four factors affecting the six-component force coefficient of the three-axle truck and the crosswind load are considered: fluctuating average wind speed, wind direction (angle), truck driving speed, and road adhesion coefficient. A three-axle truck nonlinear model is established accordingly. The model is used to obtain the dynamic response of the three-axle truck under strong crosswind conditions as per the time-varying curves of the vertical load of the truck, the time-varying curves of the lateral displacement of the center of mass, and the time-varying curves of the heading angle. An advanced Monte Carlo simulation algorithm based on importance sampling is used to determine the probability of a three-axle truck with FPSS under strong crosswinds; the given acceptable probability of failure (accident) is used to obtain the critical safety speed. The sensitivity analysis of random variables reveals that the possibility of three truck failures of the three-axle truck in strong crosswinds is, from largest to smallest, rollover, side-slip, and rotation. This research may provide useful guidance for exploring the probability of wind-induced accidents and the critical safety speeds of vehicles, as well as useful general information for road transportation management departments.

Diffusion of Slightly Buoyant Droplets in Isotropic Turbulence

2006 ◽  
Author(s):  
Balaji Gopalan ◽  
Edwin Malkiel ◽  
Joseph Katz
Keyword(s):  
High Speed ◽  
Isotropic Turbulence ◽  
Three Dimensional ◽  
Time History ◽  
Region Of Interest ◽  
Frame Rate ◽  
Three Dimensions ◽  
Velocity Autocorrelation Function ◽  
Mean Flow ◽  
Dispersion Tensor

We study the diffusion of slightly buoyant droplets in isotropic turbulence using High Speed Digital Holographic PIV. Droplets (Specific Gravity 0.85) are injected in the central portion of an isotropic turbulence facility with weak mean flow. Perpendicular digital inline holograms are recorded in a 37 × 37 × 37 mm3 region of interest using two high speed cameras. Data are recorded at 250 frames per second (2000 frames per second is the maximum possible frame rate). An automated program is developed to obtain two dimensional tracks of the droplets from two orthogonal images and match them to get three dimensional tracks. Cross correlation of droplet images are used for measuring their velocities. The time series are low pass filtered to obtain accurate time history of droplet velocities. Data analysis determines the PDF of velocity and acceleration in three dimensions. The time history also enables us to calculate the three dimensional Lagrangian velocity autocorrelation function for different droplet radii. Integration of these functions gives us the diffusion coefficients. For shorter time scales, when the diffusion need not be Fickian we can use the three dimensional trajectories to calculate the generalized dispersion tensor and measure the time elapsed for diffusion to become Fickian.

scholarly journals Compression of MRI brain images based on automatic extraction of tumor region

2021 ◽  
Vol 11 (5) ◽  
pp. 3964
Author(s):  
Prakash Tunga P. ◽  
Vipula Singh
Keyword(s):  
Brain Magnetic Resonance Imaging ◽  
Region Of Interest ◽  
Set Partitioning ◽  
Arithmetic Coding ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Automatic Extraction ◽  
Performance Parameters ◽  
Compression Technique ◽  
Tumor Region

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes.

scholarly journals Automatic Extraction of Two Regions of Creases from Palmprint Images for Biometric Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Roszaharah Yaacob ◽  
Chok Dong Ooi ◽  
Haidi Ibrahim ◽  
Nik Fakhuruddin Nik Hassan ◽  
Puwira Jaya Othman ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Salient Feature ◽  
Personal Identification ◽  
Experimental Results ◽  
Biometric Identification ◽  
Automatic Extraction ◽  
Morphological Operations ◽  
Average Sensitivity ◽  
Speed Up ◽  
Sensitivity Specificity

Palmprint has become one of the biometric modalities that can be used for personal identification. This modality contains critical identification features such as minutiae, ridges, wrinkles, and creases. In this research, feature from creases will be our focus. Feature from creases is a special salient feature of palmprint. It is worth noting that currently, the creases-based identification is still not common. In this research, we proposed a method to extract crease features from two regions. The first region of interest (ROI) is in the hypothenar region, whereas another ROI is in the interdigital region. To speed up the extraction, most of the processes involved are based on the processing of the image that has been a downsampled image by using a factor of 10. The method involved segmentations through thresholding, morphological operations, and the usage of the Hough line transform. Based on 101 palmprint input images, experimental results show that the proposed method successfully extracts the ROIs from both regions. The method has achieved an average sensitivity, specificity, and accuracy of 0.8159, 0.9975, and 0.9951, respectively.

scholarly journals PROTOTYPE REGION OF INTEREST (ROI) CITRA WAJAH MANUSIA BERBASIS BINARY LARGE OBJECT (BLOB) ANALYSIS

2020 ◽  
Vol 16 (2) ◽  
pp. 83
Author(s):  
Arief Bramanto Wicaksono Putra ◽  
Mirza Rafdi Rosada ◽  
Achmad Fanany Onnilita Gaffar
Keyword(s):  
Euclidean Distance ◽  
Region Of Interest ◽  
Large Object ◽  
Blob Analysis ◽  
Flood Fill

<p>Setiap manusia memiliki identitasnya masing-masing, dan tidak akan sama satu identitas seseorang dengan identitas lainnya. Biometrik suatu wajah tidak akan sama dengan wajah lainnya, oleh karena itu dirancang suatu <em>prototype </em>pengenalan identitas ciri wajah pada wilayah-wilayah tertentu atau <em>Region of Interest </em>(ROI). ROI yang digunakan merupakan biometrik-biometrik unik yang terdapat pada wajah. Untuk mendapatkan ROI, proses segmentasi yang digunakan diantaranya adalah: Morfologi, <em>Flood fill Algorithm </em>dan <em>Tresholding. </em>Kemudian dengan menggunakan <em>BLOB Analysis</em> jumlah area dan nilai piksel yang terdapat pada ROI yang telah tersegmentasi akan dijadikan sebagai ekstraksi ciri pengenalan yang kemudian akan teridentifikasi menggunakan pendekatan <em>Euclidean distance</em>. ROI yang diperoleh dari ekstraksi menggunakan <em>BLOB analysis</em> mencakup 6 sampai 9 area biometrik wajah seperti alis, mata, hidung, mulut dan telinga. Hasil performansi dari identifikasi kemiripan wajah menggunakan 3 data wajah dengan 5 data sampel berbeda pada masing-masing wajah adalah 33,3%.</p>

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