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Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022 Computer vision-based techniques for structural displacement measurement are rapidly becoming
popular in civil structural engineering. However, most existing computer vision-based displace-
ment measurement methods require man-made targets for object matching or tracking, besides
usually the measurement accuracies are seriously sensitive to the ambient illumination variations.
A computer vision-based illumination robust and multi-point simultaneous measuring method is
proposed for structural displacement measurements. The method consists of two part, one is for
segmenting the beam body from its background, the segmentation is perfectly carried out by fully
convolutional network (FCN) and conditional random field (CRF); another is digital image cor-
relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory.
The accuracy and illumination robustness are verified through three groups of elaborately
designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation,
numbers of locations along with the segmented beam body can be chosen and measured simul-
taneously. It is verified that the method is illumination robust since the displacement measure-
ments are with the smallest fluctuations to the illumination variations. The proposed method does
not require any man-made targets attached on the structure, but because of the exploitation of
DIC in displacement measurement, the regions centered on the measuring points need to have
texture feature. keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation |
مقاله انگلیسی |
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Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications
مدل ترکیبی مبتنی بر باور عمیق برای سیستم بیومتریک چند حالته برای برنامه های امنیتی آینده-2021 Biometrics is the technology to identify humans uniquely based on face, iris, and fingerprints, etc. Biometric authentication allows the person recognition automatically on the basis of behavioral or physiological charac- teristics. Biometrics are broadly employed in several commercial as well as the official identification systems for automatic access control. This paper introduces the model for multimodal biometric recognition based on score level fusion method. The overall procedure of the proposed method involves five steps, such as pre-processing, feature extraction, recognition score using Multi- support vector neural network (Multi-SVNN) for all traits, score level fusion, and recognition using deep belief neural network (DBN). The first step is to input the training images into pre-processing steps. Thus, the pre-processing of three traits, like iris, ear, and finger vein is done. Then, the feature extraction is done for each modality to extract the features. After that, the texture features are extracted from pre-processed images of the ear, iris, and finger vein, and the BiComp features are acquired from individual images using a BiComp mask. Then, the recognition score is computed based on the Multi-SVNN classifier to provide the score individually for all three traits, and the three scores are provided to the DBN. The DBN is trained using the chicken earthworm optimization algorithm (CEWA). The CEWA is the integration of the chicken swarm optimization (CSO), and earthworm optimization algorithm (EWA) for the optimal authentication of the person. The analysis proves that the developed method acquired a maximal accuracy of 95.36%, maximal sensitivity of 95.85%, and specificity of 98.79%, respectively. Keywords: Multi-modal Bio-metric system | Chicken Swarm Optimization | Earthworm Optimization algorithm | Deep Belief Network | Multi-SVNN |
مقاله انگلیسی |
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Online detection of naturally DON contaminated wheat grains from China using Vis-NIR spectroscopy and computer vision
تشخیص آنلاین دانه های گندم آلوده به DON طبیعی از چین با استفاده از طیف سنجی Vis-NIR و بینایی ماشین-2021 Deoxynivalenol (DON) contamination of wheat grains is a serious problem in China, and it
is necessary to remove contaminated wheat before it enters the consumer market. In this
study, visible-near infrared (Vis-NIR) spectroscopy and computer vision techniques were
combined to simulate online discrimination between normal and DON-contaminated
wheat grains. Naturally growing wheat samples were collected from several of the main
wheat-producing areas in China, the reference DON contents were measured by using
liquid chromatography serial triple quadrupole mass spectrometer (LC-MS), and then
wheat samples were divided into two categories according to the national standard of
1 mg kg1. The characteristic spectral variables, colour and texture features were extracted
and integrated for chemometric analysis. Principal component analysis based on fusion
features indicated better clustering than with just spectral features. Subsequently, linear
discriminant analysis modelling based on spectra and texture features achieved the best
discrimination with an accuracy of 95.06% and 91.36% for calibration and validation sets
respectively, which was 5% higher than with just spectral features, and the false positive
rates (FPR) were the lowest: 3.41% and 10.42% for calibration and validation sets respectively. The internal scanning results of whole wheat flour indicated that the higher the
content of DON, the looser the binding of starch granules, which could cause the textural
change of wheat grains. The research showed that Vis-NIR spectroscopy combined with
computer vision has the potential to be used in the non-destructive and online detection of
DON-contaminated wheat grains; further study on the interference from complex environments is still need for actual online detection.
Keywords: Vis-NIR spectroscopy | Computer vision | Wheat grains | DON | Features fusion |
مقاله انگلیسی |
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In-field automatic detection of maize tassels using computer vision
تشخیص خودکار کاکل ذرت با استفاده از بینایی ماشین-2021 The heading stage of maize is an important period during its growth and development and indicates the beginning of its pollination. In this regard, an automated method for maize tassel detection is highly important to monitor maize growth. However, the recognition of maize heading stage mainly relies on visual evaluation. This method presents some limitations, such as expensive and subjective. This work proposed a novel method for automatic tassel detection. In the proposed algorithm, a color attenuation prior model was used to model the scene depth of saturation graph to remove image saturation. An Itti visual attention detection algorithm was used to detect the area of interest. Texture features and vegetation indices were used to develop a classification model to eliminate false positives. Pictures were captured using a commercial camera for two years to verify the stability of the proposed algorithm. Three indices were calculated to quantitatively assess and rate the algorithms. Experimental results show that the proposed method outperforms other existing methods, and its recall, precision, and F1 measure values are 86.30%, 91.44%, and 88.36%, respectively. Results indicate that the proposed method can effectively detect maize tassels in field images and remain stable with time.© 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Maize tassel detection | Texture feature | Vegetation index | Saliency based |
مقاله انگلیسی |
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Vision based prediction of surface roughness for end milling
پیش بینی مبتنی بر دید از زبری سطح برای فرز نهایی-2021 Measurement of surface roughness helps to assess the machined component’s functionality. In the past three decades, several scientists have contributed to the computation of surface roughness. This research article deals with two distinct methods for prediction of surface roughness employing the surface pro- filometer and machine vision for AISI 1040 steel specimens prepared by varying cutting parameters of end milling viz. feed rates, speed and cutting depth. Using a surface profilometer, the surface roughness parameters are evaluated. At the other hand, the texture features were extracted using a Gray Level Co- occurrence Matrix Algorithm (GLCM) and a computer vision system. Correlations are formed among characteristics of machined surface and the texture feature such as contrast, entropy, energy, and homogeneity. The comparable findings revealed a maximum relative error of —8% using contrast and energy, — 11% using entropy and —10% using homogeneity.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Processing & Characterization. Keywords: Surface roughness parameter | Gray Level Co-occurrence Matrix (GLCM) | Texture feature | Machine vision system | Linear regression |
مقاله انگلیسی |
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The biometric recognition system based on near-infrared finger vein image
سیستم تشخیص بیومتریک بر اساس تصویر رگ انگشت نزدیک مادون قرمز-2021 It is a difficult task to extract vein features accurately since the finger-vein images captured by near infrared light are always poor in quality. This paper proposes a novel finger vein feature representation scheme based on pyramid histograms of oriented gradients and local phase quantization. As the vein networks consist of abundant texture and orientation features, a texture feature description operator at various scales is employed on the finger vein image to reduce the effects of geometric deformation occurred image acquisition due to the different posture and position of fingers. To solve the adverse effects of image blurring caused by uneven illumination, local phase quantization is then introduced to extract vein features. Finally, the above mentioned extracted two kinds of texture characteristics of vein image are fused at feature level by concatenated histograms to obtain accurate vein feature named pyramid local phase quantization histogram (PLPQ). In this way, we encode the vein image in- formation not only in frequency domain but also among different orientations and scales. We perform rigorous experiments on two publicly available databases named FV-USM and MMCBUN, and the results of the experiments reveal that proposed fusion system can make promising improvement of finger vein recognition performance. Keywords: Finger texture | Finger vein recognition | Pyramid histogram of oriented gradients | Local phase quantization |
مقاله انگلیسی |
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QUANTITATIVE ULTRASOUND AND B-MODE IMAGE TEXTURE FEATURES CORRELATE WITH COLLAGEN AND MYELIN CONTENT IN HUMAN ULNAR NERVE FASCICLES
ويژگي هاي بافت تصفيفي ULTRASOUND و B-MODE كمتر با كلاژن و محتوا ميلين در فاكتورهاي NERVE ULNAR انسان-2019 Abstract—We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization
of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal
30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched
between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular
region of interest. We used histology sections to determine features related to the concentration of collagen and
myelin and ultrasound data to calculate the backscatter coefficient (24.89 § 8.31 dB), attenuation coefficient
(0.92 § 0.04 db/cm-MHz), Nakagami parameter (1.01 § 0.18) and entropy (6.92 § 0.83), as well as B-mode texture
features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations
between the combined collagen and myelin concentrations were obtained for the backscatter coefficient
(R = 0.68), entropy (R = 0.51) and several texture features. Our study indicates that quantitative ultrasound
may potentially provide information on structural components of nerve fascicles. Key Words: Nerve | Quantitative ultrasound | High frequency | Histology | Pattern recognition | Texture analysis |
مقاله انگلیسی |
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Machine learning methods for MRI biomarkers analysis of pediatric posterior fossa tumors
روشهای یادگیری ماشین برای تحلیل نشانگرهای زیستی MRI از تومورهای حفره ای کودکان-2019 Medical imaging technologies provide an increasing number of opportunities for disease
prediction and prognosis. Specifically, imaging biomarkers can quantify the entire tumor
phenotypes to enhance the prediction. Machine learning technology can be explored to mine
and analyze these biomarkers and to establish predictive models for the clinical applications.
Several studies have applied various machine learning methods to imaging biomarkers
based clinical predictions of different diseases. Here we seek to evaluate different
machine learning methods in pediatric posterior fossa tumor prediction. We present a
machine learning based magnetic resonance imaging biomarkers analysis framework for
two kinds of pediatric posterior fossa tumors. In details, three feature extraction methods
are used to obtain 300 imaging biomarkers. 10 feature selection methods and 11 classifiers
are evaluated by the quantified predictive performance and stability, and importance
consistency of features and the influence of the experimental factors are also analyzed.
Our results demonstrate that the CFS feature selection method (accuracy: 83.85 5.51%, stability: [0.84, 0.06]) and SVM classifier (accuracy: 85.38
3.47%, RSD: 4.77%) show relatively better performance than others and should be preferred. Among all the biomarkers, 17
texture features seem to be more important. Multifactor analysis results indicate the choice
of classifier accounts for the most contribution to the variability in performance (37.25%).
The machine learning based framework is efficient for pediatric posterior fossa tumors
biomarkers analysis and could provide valuable references and decision support for assisted
clinical diagnosis. Keywords: Pediatric posterior fossa tumor | Magnetic resonance imaging | Biomarker | Machine learning | Feature selection | Classifier |
مقاله انگلیسی |
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A transfer learning method with deep residual network for pediatric pneumonia diagnosis
یک روش یادگیری انتقال با شبکه باقی مانده عمیق برای تشخیص پنومونی کودکان-2019 Background and Objective: Computer aided diagnosis systems based on deep learning and medical imag- ing is increasingly becoming research hotspots. At the moment, the classical convolutional neural network generates classification results by hierarchically abstracting the original image. These abstract features are less sensitive to the position and orientation of the object, and this lack of spatial information limits the further improvement of image classification accuracy. Therefore, how to develop a suitable neural net- work framework and training strategy in practical clinical applications to avoid this problem is a topic that researchers need to continue to explore. Methods: We propose a deep learning framework that combines residual thought and dilated convolution to diagnose and detect childhood pneumonia. Specifically, based on an understanding of the nature of the child pneumonia image classification task, the proposed method uses the residual structure to overcome the over-fitting and the degradation problems of the depth model, and utilizes dilated convolution to overcome the problem of loss of feature space information caused by the increment in depth of the model. Furthermore, in order to overcome the problem of difficulty in training model due to insufficient data and the negative impact of the introduction of structured noise on the performance of the model, we use the model parameters learned on large-scale datasets in the same field to initialize our model through transfer learning. Results: Our proposed method has been evaluated for extracting texture features associated with pneu- monia and for accurately identifying the performance of areas of the image that best indicate pneumonia. The experimental results of the test dataset show that the recall rate of the method on children pneu- monia classification task is 96.7%, and the f1-score is 92.7%. Compared with the prior art methods, this approach can effectively solve the problem of low image resolution and partial occlusion of the inflam- matory area in children chest X-ray images. Conclusions: The novel framework focuses on the application of advanced classification that directly per- forms lesion characterization, and has high reliability in the classification task of children pneumonia Keywords: Pneumonia | Deep learning | Residual network | Image classification | Transfer Learning |
مقاله انگلیسی |
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Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI
تمایز متاستازهای ستون فقرات ناشی از سرطانهای ریه و سایر سرطانها با استفاده از رادیولوژی و یادگیری عمیق بر اساس DCE-MRI-2019 Purpose: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers
using radiomics and deep learning, compared to traditional hot-spot ROI analysis.
Methods: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE)
sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung;
31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic
parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor
mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram
and texture features from three DCE parametric maps. Deep learning was performed using these maps as
inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a
convolutional long short term memory (CLSTM) network.
Results: For hot-spot ROI analysis, mean wash-out slope was 0.25 ± 10% for lung metastases and
−9.8 ± 12.9% for other tumors. CHAID classification using a wash-out slope of −6.6% followed by wash-in
enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing
tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a
mean accuracy of 0.71 ± 0.043, whereas a CLSTM improved accuracy to 0.81 ± 0.034.
Conclusions: DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in
the spine, which may be used to guide subsequent workup for confirmed diagnosis. Keywords: DCE-MRI | Radiomics | Deep learning | Spinal metastases |
مقاله انگلیسی |