Image Processing Techniques and Neuro-computing Algorithms in Computer Vision

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method’s usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

Recent technological advancements allow deep learning to be employed in practically every aspect of life. Because deep learning techniques are so precise, they can be used in medicine to classify and detect various diseases. The coronavirus (SARSCoV2) epidemic has recently affected global health systems. One of the ways to diagnose SARSCoV2 is with a chest X-ray. This paper proposes a deep learning technique to distinguish SARSCoV2 positive and normal cases. In this study, we fine-tuned the deep learning models and hyperparameters, and the fine-tuned deep learning models performed significantly better. To classify X-ray images, we developed a system based on deep learning algorithms that includes five models: Xception, VGG19, ResNet50, DenseNet121, and Inception. We offer deep learning models and algorithms that have been trained and evaluated to support medical efforts and reduce medical staff workload when dealing with SARSCoV2. In addition, the classification model that was proposed yields positive results because it makes use of accurate classification of the SARSCoV2 disease based on medical images. Additionally, the performance of our proposed CNN classification method for medical imaging was evaluated using various edge-based neural networks. The accuracy of tertiary classification with CNN will decrease as the number of classes in the training network grows. In tertiary classification, which includes normal and SARSCoV2 positive images, the proposed model achieved a 0.9897 accuracy. The proposed algorithm achieves a high level of classification accuracy when using the DenseNet121 model for binary classification.

In the last few years, huge amounts of progress have been made regarding remote sensing in the field of computer vision. This success and progress is mostly due to the effectiveness of deep learning (DL) algorithms. In addition, the remote sensing community has shifted its attention to DL, and DL algorithms have been used to achieve significant success in many image analysis tasks. However, with regard to remote sensing, a number of challenges caused by difficulties in data acquisition and annotation have not been fully solved yet. This reprint is a collection of novel developments in the field of remote sensing using computer vision, deep learning, and artificial intelligence. The articles published involve fundamental theoretical analyses as well as those demonstrating their application to real-world problems.

Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects—order, family, and genus—and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.

Deep learning has gained immense popularity in scientific computing, and its algorithms are widely used in complex problem-solving industries. Every deep learning algorithm use different types of neural networks to perform indented tasks. Deep learning (DL) algorithms have emerged from different machine learning and soft computing methodologies. Since then, a number of deep learning (DL) algorithms have been recently introduced in the scientific community and applied in various application fields. Today, the use of DLs has become indispensable due to their intelligence, effective learning, accuracy and reliability in model creation. However, a comprehensive list of DL algorithms has not yet been presented in the scientific literature. This article lists the most popular DL algorithms and their application areas. Deep learning uses ANN artificial neural networks to perform convoluted calculations on huge amounts of data. It is a type of machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment and advertising often use deep learning.

Unfolding Explainable AI for Brain Tumor Segmentation

This study explored the transformative effects of AI-driven facial recognition technology on identity verification systems. It delves into the recent progress in computer vision and deep learning algorithms, which have greatly improved the accuracy and speed of facial recognition. This study highlights the potential uses of this technology in various fields such as security, finance, and personalized services. It also examines the ethical issues and privacy concerns arising from the widespread adoption of facial recognition. Furthermore, this study addresses the challenges of creating robust systems that can manage diverse populations and environmental conditions. Finally, it outlines future research and development directions for facial recognition technology, stressing the importance of responsible innovation and regulatory frameworks. Keywords — Face Recognition, Artificial Intelligence, Computer Vision, Identity Verification, Biometrics, Privacy, Security, Deep Learning, Feature Extraction, Multimodal Biometrics.

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Feature mining for encrypted malicious traffic detection with deep learning and other machine learning algorithms

Improving the performance of machine learning algorithms to overcome the curse of dimensionality while maintaining interpretability is still a challenging issue for researchers in artificial intelligence. Patch learning (PL), based on the improved adaptive network-based fuzzy inference system (ANFIS) and continuous local optimization for the input domain, is characterized by high accuracy. However, PL can only handle low-dimensional data set regression. Based on the parallel and serial ensembles, two deep patch learning algorithms with embedded adaptive fuzzy systems (DPLFSs) are proposed in this paper. First, using the maximum information coefficient (MIC) and Pearson's correlation coefficients for feature selection, the variables with the least relationship (linear or nonlinear) are excluded. Second, principal component analysis is used to reduce the complexity further of DPLFSs. Meanwhile, fuzzy C-means clustering is used to enhance the interpretability of DPLFSs. Then, an improved PL method is put forward for the training of each sub-fuzzy system in a fashion of bottom-up layer-by-layer, and finally, the structure optimization is performed to significantly improve the interpretability of DPLFSs. Experiments on several benchmark data sets show the advantages of a DPLFS: (1) it can handle medium-scale data sets; (2) it can overcome the curse of dimensionality faced by PL; (3) its precision and generalization are greatly improved; and (4) it can overcome the poor interpretability of deep learning networks. Compared with shallow and deep learning algorithms, DPLFSs have the advantages of interpretability, self-learning, and high precision. DPLFS1 is superior for medium-scale data; DPLFS2 is more efficient and effective for high-dimensional problems, has a faster convergence, and is more interpretable.

The human brain is a remarkably complex organ. Comprehension of the tissue properties of the brain and the multitude of changes in disease and ageing may lead to a greater appreciation and understanding of human biology. Combined, Magnetic Resonance Imaging (MRI) and image processing techniques are now capable of resolving very high-resolution images of the brain, including characterising discrete tissue types within small brain structures such as the hippocampus. The hippocampus is a small structure most commonly associated with its function in memory, and is comprised of subfields with distinct cellular compositions (or laminae). Computational techniques for delineating tissue types within the hippocampus incorporate sophisticated computer vision and medical imaging algorithms to better describe the range of changes occurring in these structures. In parallel, improved MR scanner hardware and sequence choices including leveraging multiple contrasts of the same tissue in vivo allows for complementary imaging information of the structure, leading to a more specific and reliable characterisation of brain tissue. Despite advances in the field, several competing algorithms and techniques are used for hippocampus subfield morphometry properties such as volume and shape.This thesis will explore computational techniques for characterising human brain tissue in vivo using multi-contrast, high resolution, ultra high-field MRI data. Specifically, this thesis will focus on characterising the tissue of the hippocampus as an exemplar for the heterogeneity of tissue classes, complex shape changes, and large, variable individual differences in anatomy between healthy and diseased/disordered subjects. This thesis will pay specific attention to characterising hippocampus tissue (in particular, image segmentation and volumetry) in a wide range of subjects and patients, which is often ignored in computational techniques for analysing MRI data.First, techniques for retrospectively ameliorating within-session motion in MRI in both healthy participants and patients with Motor Neurone Disease (MND) will be examined and discussed. It was found that incorporating a computational technique for image registration improved the reliability of image segmentation of the subfields of the hippocampus.Next, in order to better characterise the changes in anatomy over time using MRI, we constructed and evaluated a longitudinal pipeline for segmenting and examining the volume of hippocampal subfields using computational techniques in a healthy participant pool and patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To aid in this research, the Towards Optimising MRI ChAracterisation of Tissue (TOMCAT) data set was acquired using Ultra-High Field (7-Tesla) MRI. Thirdly, to better characterise the subfields of the hippocampus and in less time than traditional computational techniques, a deep learning approach to hippocampus subfield segmentation using multi-contrast, high-resolution MRI was developed and evaluated. We found that deep learning techniques may aid in speeding the process of hippocampus subfield segmentation and deliver accurate results.Finally, the clinical applications of the previous studies were explored. We detail automated methods for extracting precise measures describing hippocampal subfield morphology and compare these features in MND patients and healthy controls. Brain tissue can be successfully characterised in vivo using high resolution and multi-contrast MRI. Developing robust and reproducible methods that are useful in both research and clinical contexts is an important step in demystifying biological processes involved in ageing and contributing to disorder and disease. This thesis adds to and improves the broader knowledge base of computational techniques for characterising brain tissue and adds novel and enhanced methodology to the field of neuroimaging.

BackgroundMachine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management.PurposeTo develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors.MethodsWe constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables.ResultsDL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below).ConclusionsIn the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting.Funding AcknowledgementType of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1TIMI performance on validation setDL performance on validation set

Corrosion causes irreversible damage to metal structures and is one of the most common causes of bridge failure. The advent of affordable unmanned aerial vehicles (UAVs) that can carry digital cameras close to a structure presents an opportunity to quickly inspect bridges for corrosion. However, imagery-based corrosion assessment techniques must be accurate, detailed, and comprehensive to be effectively used in bridge inspections. This paper reviews and evaluates the use of imagery data and machine learning for corrosion assessment on bridges, analyzing existing research on imagery-based corrosion assessment, examining the ongoing difficulties of proposed data analysis approaches, and determining the remaining gaps in the literature. This paper evaluates the existing work with an emphasis on image processing methods; the environment and conditions in which the methods have been tested; evaluation of metrics used for performance assessment; and distinctions between corrosion detection, localization, and segmentation, as are significant in supporting bridge inspection decisions. The analysis finds that image processing techniques are typically too prone to false positives to perform well on chaotic data, particularly with nonuniform lighting and misleading objects in the image, which would be collected during bridge inspections using UAVs. Deep learning algorithms have been reported to perform more accurately than the human benchmark on similar data, and multiple researchers have developed deep learning algorithms that work well on UAV-collected data. However, there remain gaps in performance between methods tested on laboratory data compared to field data and on human-collected compared to UAV-collected data. Algorithms need to be trained on larger, more variable data sets to demonstrate generalizability across bridge inspection applications. If addressed, these methods could improve the safety, time-, and cost-efficiency of bridge inspections; help inspectors understand the state of each asset; and support structural repair, reconstruction, and prioritization decisions before failures occur.

Evaluation of deep learning for detecting intraosseous jaw lesions in cone beam computed tomography volumes

The global pandemic coronavirus disease (COVID-19) is the reason behind all the chaos happening in medical world right now. There is a sudden surge of need for medical facilities around the globe, and prompted medical staff along with patients and families has very less time and information to take proper decisions, it is no surprise that hospitals are using computer vision, and artificial intelligence and machine learning techniques to assist with its functions. Healthcare is different from all other industries. It is a sector of high priority and people want the highest level of services, care without worrying about cost. But it is not possible to have medical experts mainly to interpret the medical data in remote areas of the country. This unprecedented crisis of COVID pandemic taught us how digital integration in healthcare is very important. It is very difficult to interpret the chest X-ray usually required to detect the level of infection in a COVID-19 patient, due to subjectivity and complexity of the images. Deep learning algorithms, such as convolutional neural networks (CNNs), are used for the automation of medical image analysis. This chapter provides study about deep learning architecture and its optimization used for medical image classification. In the pandemic of COVID-19, we can manage and improve patient care using deep learning algorithms especially in covert areas where medical experts cannot be physically present to analyze the images. In this chapter, we have discussed one such technique: image classification and applied on the dataset of the chest X-ray of patients to identify the infection of coronavirus. We will use our first CNN made using Keras and tensor flow on an open-source dataset of chest X-ray for identifying COVID-19 infection, and after building the model we will test our model using test data and try to achieve high accuracy.