Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion.

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Detection method of PCB component based on automatic optical stitching algorithm

Circuit World ◽

10.1108/cw-09-2014-0039 ◽

2015 ◽

Vol 41 (4) ◽

pp. 133-136 ◽

Cited By ~ 2

Author(s):

Ge Qiang ◽

Zheng Shanshan ◽

Zhao Yang ◽

Chen Mao

Keyword(s):

Solder Joint ◽

High Speed ◽

Feature Matching ◽

Region Of Interest ◽

Scale Space ◽

Image Stitching ◽

Small Data ◽

Full Line ◽

Content Type ◽

Line Image

Purpose – This paper aims to propose image stitching by reduction of full line and taking line image as registration image to solve the problem of automatic optic inspection in PCB detection. In addition, surf registration was introduced for image stitching to improve the accuracy and speed of stitching. Design/methodology/approach – First, image stitching proceeded by method of full line reduction and taking line image as registration image; second, surf registration was introduced based on the traditional PCB image stitching algorithm. Scale space of the image pyramid was adopted for confirming relative future points between stitching image. The registration means of nearest neighbourhood and next neatest neighborhood was selected for feature matching and fused in region of interest to fulfil image stitching. Findings – The improved stitching algorithm with small data size of image, high speed and noncumulative transitive error eliminated displacement deviation and solved the stitching gap caused by uneven illumination, to greatly improve the accuracy and speed of stitching. Research limitations/implications – The research of this paper can only used for appearance detection and cannot be used for solder joint inspection with circuit detection or invisible solder joint detection; it can identify and mark PCB component defects but cannot classify automatically, thus artificial confirmation and processing is needed. Originality/value – Based on the traditional image stitching means, this paper proposed full line reduction for image stitching, which reduces processing of data and speeds up image stitching; in addition, surf registration was introduced into the study of PCB stitching algorithm, which greatly improves the accuracy and speed of stitching and solves stitching gap formed by opposite variation trend of image local edge caused by uneven illumination.

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Improved Parallax Image Stitching Algorithm Based on Feature Block

Symmetry ◽

10.3390/sym11030348 ◽

2019 ◽

Vol 11 (3) ◽

pp. 348 ◽

Cited By ~ 3

Author(s):

Huaitao Shi ◽

Lei Guo ◽

Shuai Tan ◽

Gang Li ◽

Jie Sun

Keyword(s):

Feature Matching ◽

Computational Cost ◽

Reference Image ◽

Image Stitching ◽

Feature Points ◽

Shape Distortion ◽

Scale Invariant ◽

Point Matching ◽

Feature Point Matching ◽

Scale Invariant Feature

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.

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A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190115155223 ◽

2020 ◽

Vol 16 (3) ◽

pp. 199-205

Author(s):

Yuejun Liu ◽

Yifei Xu ◽

Xiangzheng Meng ◽

Xuguang Wang ◽

Tianxu Bai

Keyword(s):

Deep Learning ◽

Learning Algorithms ◽

Region Of Interest ◽

Classification Performance ◽

Thyroid Diseases ◽

Great Success ◽

Learning Models ◽

Good Classification Performance ◽

Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽

10.3390/w13010087 ◽

2021 ◽

Vol 13 (1) ◽

pp. 87

Author(s):

Yongqiang Wang ◽

Ye Liu ◽

Xiaoyi Ma

Keyword(s):

Surrogate Model ◽

Computational Cost ◽

Region Of Interest ◽

Optimization Procedure ◽

Optimal Shape ◽

Small Sample ◽

Gravity Dam ◽

Kriging Surrogate Model ◽

Gravity Dams ◽

Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

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Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Communications Biology ◽

10.1038/s42003-020-01638-1 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Gaoyang Li ◽

Haoran Wang ◽

Mingzi Zhang ◽

Simon Tupin ◽

Aike Qiao ◽

...

Keyword(s):

Deep Learning ◽

Artery Bypass ◽

Computational Cost ◽

Specific Model ◽

Arterial System ◽

Patient Specific ◽

Cardiovascular Hemodynamics ◽

Before And After ◽

Deep Learning Network ◽

Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

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Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽

10.3390/vibration4010004 ◽

2020 ◽

Vol 4 (1) ◽

pp. 49-63

Author(s):

Waad Subber ◽

Sayan Ghosh ◽

Piyush Pandita ◽

Yiming Zhang ◽

Liping Wang

Keyword(s):

Dynamical Systems ◽

Computational Cost ◽

Three Dimensional ◽

Region Of Interest ◽

System Response ◽

Computational Domain ◽

User Interest ◽

Numerical Resolution ◽

Multi Scale ◽

Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

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Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

Applied Sciences ◽

10.3390/app11041965 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1965

Author(s):

Raul-Ronald Galea ◽

Laura Diosan ◽

Anca Andreica ◽

Loredana Popa ◽

Simona Manole ◽

...

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Diagnostic System ◽

Region Of Interest ◽

General Purpose ◽

Medical Image Segmentation ◽

Dice Similarity Coefficient ◽

Learning Methods ◽

Study Results ◽

Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

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A low complexity image stitching using advanced feature matching in mobile environments

Journal of Computer Virology and Hacking Techniques ◽

10.1007/s11416-014-0207-x ◽

2014 ◽

Vol 10 (2) ◽

pp. 129-136 ◽

Cited By ~ 2

Author(s):

Hyochang Ahn ◽

Yong-Hwan Lee ◽

June-Hwan Lee ◽

Han-Jin Cho

Keyword(s):

Feature Matching ◽

Low Complexity ◽

Image Stitching ◽

Mobile Environments

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Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models

Symmetry ◽

10.3390/sym13030511 ◽

2021 ◽

Vol 13 (3) ◽

pp. 511

Author(s):

Syed Mohammad Minhaz Hossain ◽

Kaushik Deb ◽

Pranab Kumar Dhar ◽

Takeshi Koshiba

Keyword(s):

State Of The Art ◽

Computational Cost ◽

Region Of Interest ◽

Number Of Clusters ◽

Plant Leaf ◽

Leaf Disease ◽

Automatic Initialization ◽

Adequate Accuracy ◽

Model Size ◽

High Computational Cost

Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

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