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Machine Learning in Computer Vision and Image Sensing: Theory and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 39131

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Dr. Subrata Chakraborty

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1. School of Science and Technology, Faculty of SABL, University of New England, Armidale, NSW 2351, Australia
2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW 2007, Australia
3. Griffith Business School, Griffith University, Brisbane, QLD 4111, Australia
Interests: optimisation models; data analytics; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Biswajeet Pradhan

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Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: radar image processing remote sensing and GIS applications GIS for engineers forecasting disaster hazard; stochastic analysis and modelling; natural hazards; environmental engineering modelling; geospatial information systems; photogrammetry and remote sensing; unmanned aerial vehicles (UAVs).
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning models have seen tremendous progress over the past decade, with hugely diverse application domains such as Earth observation, spatial computing, natural hazard and environmental modeling, medical imaging, and many more. Computer vision and image sensing area have benefitted immensely from machine learning applications to solve complex and real-world challenges. Advances in machine learning enable us to better analyse image and sensor data. This has driven enormous research endeavours towards solving practical problems in multiple application domain areas including healthcare, agriculture, defence, remote sensing, Earth observation, autonomous navigation, etc. This Special Issue aims to collate a compendium of top-quality research works addressing the broad challenges in both theoretical and application aspects of advanced machine learning models in computer vision and image sensing areas.

The key topics of interest include (but are not limited to):

Machine learning theory for computer vision and image sensing:

Developing machine learning models
Data pre-processing, classification, detection, segmentation
Managing high resolution data
Model explainability and bias handling

Machine learning application for computer vision and image sensing:

Healthcare and biomedical imaging
Remote sensing and satellite imaging in earth and environmental modelling
Multimodal (image + other data) machine learning
Other emerging applications

Dr. Subrata Chakraborty
Prof. Dr. Biswajeet Pradhan
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Machine learning
Deep learning
Computer vision
Image classification
Image analysis
Image segmentation
Medical imaging
Remote sensing
Satellite imaging
Drone imaging

Published Papers (11 papers)

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Research

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16 pages, 3120 KiB

Open AccessArticle

Non-Deep Active Learning for Deep Neural Networks

by Yasufumi Kawano, Yoshiki Nota, Rinpei Mochizuki and Yoshimitsu Aoki

Sensors 2022, 22(14), 5244; https://doi.org/10.3390/s22145244 - 13 Jul 2022

Cited by 1 | Viewed by 1659

Abstract

One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabeled images. Based on this prediction, an uncertainty indicator is generated for each unlabeled image. Images with a high uncertainty index are considered to have a high information content, and are selected for annotation. Our proposed method is based on a very simple and powerful idea: select samples near the decision boundary of the model. Experimental results on multiple datasets show that the proposed method achieves higher accuracy than conventional active learning methods on multiple tasks and up to 14 times faster execution time from

1.2 \times 10^{6}

s to

8.3 \times 10^{4}

s. The proposed method outperforms the current SoTA method by 1% accuracy on CIFAR-10. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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20 pages, 37554 KiB

Open AccessArticle

ROSEBUD: A Deep Fluvial Segmentation Dataset for Monocular Vision-Based River Navigation and Obstacle Avoidance

by Reeve Lambert, Jalil Chavez-Galaviz, Jianwen Li and Nina Mahmoudian

Sensors 2022, 22(13), 4681; https://doi.org/10.3390/s22134681 - 21 Jun 2022

Cited by 4 | Viewed by 2539

Abstract

Obstacle detection for autonomous navigation through semantic image segmentation using neural networks has grown in popularity for use in unmanned ground and surface vehicles because of its ability to rapidly create a highly accurate pixel-wise classification of complex scenes. Due to the lack of available training data, semantic networks are rarely applied to navigation in complex water scenes such as rivers, creeks, canals, and harbors. This work seeks to address the issue by making a one-of-its-kind River Obstacle Segmentation En-Route By USV Dataset (ROSEBUD) publicly available for use in robotic SLAM applications that map water and non-water entities in fluvial images from the water level. ROSEBUD provides a challenging baseline for surface navigation in complex environments using complex fluvial scenes. The dataset contains 549 images encompassing various water qualities, seasons, and obstacle types that were taken on narrow inland rivers and then hand annotated for use in semantic network training. The difference between the ROSEBUD dataset and existing marine datasets was verified. Two state-of-the-art networks were trained on existing water segmentation datasets and tested for generalization to the ROSEBUD dataset. Results from further training show that modern semantic networks custom made for water recognition, and trained on marine images, can properly segment large areas, but they struggle to properly segment small obstacles in fluvial scenes without further training on the ROSEBUD dataset. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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22 pages, 6531 KiB

Open AccessArticle

A³C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer

by Nadiah A. Baghdadi, Amer Malki, Hossam Magdy Balaha, Mahmoud Badawy and Mostafa Elhosseini

Sensors 2022, 22(11), 4250; https://doi.org/10.3390/s22114250 - 02 Jun 2022

Cited by 12 | Viewed by 2616

Abstract

Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A

^{3}

C-TL-GTO framework for MRI image classification and AD detection. The A

^{3}

C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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23 pages, 11087 KiB

Open AccessArticle

Pedestrian Detection Using Integrated Aggregate Channel Features and Multitask Cascaded Convolutional Neural-Network-Based Face Detectors

by Jing Yuan, Panagiotis Barmpoutis and Tania Stathaki

Sensors 2022, 22(9), 3568; https://doi.org/10.3390/s22093568 - 07 May 2022

Cited by 1 | Viewed by 1361

Abstract

Pedestrian detection is a challenging task, mainly owing to the numerous appearances of human bodies. Modern detectors extract representative features via the deep neural network; however, they usually require a large training set and high-performance GPUs. For these cases, we propose a novel human detection approach that integrates a pretrained face detector based on multitask cascaded convolutional neural networks and a traditional pedestrian detector based on aggregate channel features via a score combination module. The proposed detector is a promising approach that can be used to handle pedestrian detection with limited datasets and computational resources. The proposed detector is investigated comprehensively in terms of parameter choices to optimize its performance. The robustness of the proposed detector in terms of the training set, test set, and threshold is observed via tests and cross dataset validations on various pedestrian datasets, including the INRIA, part of the ETHZ, and the Caltech and Citypersons datasets. Experiments have proved that this integrated detector yields a significant increase in recall and a decrease in the log average miss rate compared with sole use of the traditional pedestrian detector. At the same time, the proposed method achieves a comparable performance to FRCNN on the INRIA test set compared with sole use of the Aggregated Channel Features detector. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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23 pages, 6436 KiB

Open AccessArticle

Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images

by Priyanka Malhotra, Sheifali Gupta, Deepika Koundal, Atef Zaguia, Manjit Kaur and Heung-No Lee

Sensors 2022, 22(6), 2278; https://doi.org/10.3390/s22062278 - 15 Mar 2022

Cited by 16 | Viewed by 4690

Abstract

Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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12 pages, 1063 KiB

Open AccessArticle

Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving

by Yu-Bang Chang, Chieh Tsai, Chang-Hong Lin and Poki Chen

Sensors 2021, 21(23), 8072; https://doi.org/10.3390/s21238072 - 02 Dec 2021

Cited by 3 | Viewed by 2001

Abstract

As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply the deep learning model to edge devices accompanying sensors on vehicles, we need to design a structure that has the best trade-off between accuracy and inference time. In previous works, several methods sacrificed accuracy to obtain a faster inference time, while others aimed to find the best accuracy under the condition of real time. Nevertheless, the accuracies of previous real-time semantic segmentation methods still have a large gap compared to general semantic segmentation methods. As a result, we propose a network architecture based on a dual encoder and a self-attention mechanism. Compared with preceding works, we achieved a 78.6% mIoU with a speed of 39.4 FPS with a 1024 × 2048 resolution on a Cityscapes test submission. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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23 pages, 4378 KiB

Open AccessArticle

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade’s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features

by Smita Khade, Shilpa Gite, Sudeep D. Thepade, Biswajeet Pradhan and Abdullah Alamri

Sensors 2021, 21(21), 7408; https://doi.org/10.3390/s21217408 - 08 Nov 2021

Cited by 10 | Viewed by 2752

Abstract

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade’s sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade’s SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade’s SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human–computer interaction and security in the cyber-physical space by improving person validation. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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23 pages, 8761 KiB

Open AccessArticle

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images

by Michael Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Douglas Gomes, Anwaar Ul-Haq and Abdullah Alamri

Sensors 2021, 21(19), 6655; https://doi.org/10.3390/s21196655 - 07 Oct 2021

Cited by 5 | Viewed by 3407

Abstract

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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Review

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18 pages, 1224 KiB

Open AccessReview

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

by Irfan Ullah Khan, Nida Aslam, Fatima M. Anis, Samiha Mirza, Alanoud AlOwayed, Reef M. Aljuaid and Razan M. Bakr

Sensors 2022, 22(12), 4570; https://doi.org/10.3390/s22124570 - 17 Jun 2022

Cited by 8 | Viewed by 2946

Abstract

A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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39 pages, 4036 KiB

Open AccessReview

Role of Artificial Intelligence in COVID-19 Detection

by Anjan Gudigar, U Raghavendra, Sneha Nayak, Chui Ping Ooi, Wai Yee Chan, Mokshagna Rohit Gangavarapu, Chinmay Dharmik, Jyothi Samanth, Nahrizul Adib Kadri, Khairunnisa Hasikin, Prabal Datta Barua, Subrata Chakraborty, Edward J. Ciaccio and U. Rajendra Acharya

Sensors 2021, 21(23), 8045; https://doi.org/10.3390/s21238045 - 01 Dec 2021

Cited by 31 | Viewed by 7095

Abstract

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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25 pages, 4124 KiB

Open AccessReview

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

by Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, Subrata Chakraborty, Prabal Datta Barua, Ravinesh C. Deo, Jeffrey Soar, Elizabeth E. Palmer and U. Rajendra Acharya

Sensors 2021, 21(21), 7034; https://doi.org/10.3390/s21217034 - 23 Oct 2021

Cited by 38 | Viewed by 5882

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally. Full article

(This article belongs to the Special Issue Machine Learning in Computer Vision and Image Sensing: Theory and Applications)

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