AI-Based Computer Vision Techniques and Expert Systems

Computer vision is a branch of computer science that studies how computers can ‘see’. It is a field that provides significant value for advancements in academia and artificial intelligence by processing images captured with a camera. In other words, the purpose of computer vision is to impart computers with the functions of human eyes and realise ‘vision’ among computers. Deep learning is a method of realising computer vision using image recognition and object detection technologies. Since its emergence, computer vision has evolved rapidly with the development of deep learning and has significantly improved image recognition accuracy. Moreover, an expert system can imitate and reproduce the flow of reasoning and decision making executed in human experts’ brains to derive optimal solutions. Machine learning, including deep learning, has made it possible to ‘acquire the tacit knowledge of experts’, which was not previously achievable with conventional expert systems. Machine learning ‘systematises tacit knowledge’ based on big data and measures phenomena from multiple angles and in large quantities. In this review, we discuss some knowledge-based computer vision techniques that employ deep learning.

[1]  Saptarshi Das,et al.  Hardware and Information Security Primitives Based on 2D Materials and Devices , 2022, Advanced materials.

[2]  Liangchi Zhang,et al.  A new lightweight deep neural network for surface scratch detection , 2022, The International Journal of Advanced Manufacturing Technology.

[3]  J. Li,et al.  A preliminary study on the application of deep learning methods based on convolutional network to the pathological diagnosis of PJI , 2022, Arthroplasty.

[4]  Sang Hyun Park,et al.  Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease , 2022, Frontiers in Plant Science.

[5]  Ngoc-Dung Bui,et al.  Skin Lesion Classification on Imbalanced Data Using Deep Learning with Soft Attention , 2022, Sensors.

[6]  X. Cui,et al.  Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis , 2022, Frontiers in Oncology.

[7]  S. Tabatabaei,et al.  Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients , 2022, Iranian journal of medical sciences.

[8]  Nitsa J. Herzog,et al.  Convolutional Neural Networks-Based Framework for Early Identification of Dementia Using MRI of Brain Asymmetry , 2022, Int. J. Neural Syst..

[9]  Aladdein M. Amro,et al.  Method for Determining Treated Metal Surface Quality Using Computer Vision Technology , 2022, Sensors.

[10]  M. Ljubisavljevic,et al.  Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision , 2022, Frontiers in Medicine.

[11]  Yang Xu,et al.  3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion , 2022, BMC Medical Imaging.

[12]  A. Batra,et al.  GesSure - A Robust Face-Authentication enabled Dynamic Gesture Recognition GUI Application , 2022, International Journal on Cybernetics & Informatics.

[13]  Saravana Balaji Balasubramanian,et al.  Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection , 2022, PeerJ Comput. Sci..

[14]  Syed Ali Tariq,et al.  Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography , 2022, Journal of Digital Imaging.

[15]  R. B. Faiz,et al.  Modeling and verification of authentication threats mitigation in aspect-oriented mal sequence woven model , 2022, PloS one.

[16]  Dustin S Morrow,et al.  Deep Learning for FAST Quality Assessment , 2022, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[17]  G. Spolverato,et al.  The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature , 2022, Sensors.

[18]  W. Mayol-Cuevas,et al.  Sensor-level computer vision with pixel processor arrays for agile robots , 2022, Sci. Robotics.

[19]  Amel Ksibi,et al.  Improved Analysis of COVID-19 Influenced Pneumonia from the Chest X-Rays Using Fine-Tuned Residual Networks , 2022, Computational intelligence and neuroscience.

[20]  P. Kramer.  Iconic Mathematics: Math Designed to Suit the Mind , 2022, Frontiers in Psychology.

[21]  T. Tcheng,et al.  Non-linear Embedding Methods for Identifying Similar Brain Activity in 1 Million iEEG Records Captured From 256 RNS System Patients , 2022, Frontiers in Big Data.

[22]  Weiyi Wei,et al.  Automatic recognition of micronucleus by combining attention mechanism and AlexNet , 2022, BMC Medical Informatics and Decision Making.

[23]  O. Hellwich,et al.  Knowledge-augmented face perception: Prospects for the Bayesian brain-framework to align AI and human vision , 2022, Consciousness and Cognition.

[24]  P. Keane,et al.  Artificial Intelligence and Imaging Processing in Optical Coherence Tomography and Digital Images in Uveitis , 2022, Ocular immunology and inflammation.

[25]  A. Akbulut,et al.  Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review , 2022, Sensors.

[26]  Matthew H. Brush,et al.  Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science , 2022, Clinical and translational science.

[27]  Hongya Wang,et al.  A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model , 2022, Computational intelligence and neuroscience.

[28]  Michael Tanzer,et al.  Augmented Reality in Arthroplasty: An Overview of Clinical Applications, Benefits, and Limitations. , 2022, The Journal of the American Academy of Orthopaedic Surgeons.

[29]  Murukessan Perumal,et al.  INASNET: Automatic identification of coronavirus disease (COVID-19) based on chest X-ray using deep neural network , 2022, ISA Transactions.

[30]  Xiaodong Miao,et al.  Bearing Fault Reconstruction Diagnosis Method Based on ResNet-152 with Multi-Scale Stacked Receptive Field , 2022, Sensors.

[31]  F. Rybicki,et al.  Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI , 2022, Skeletal Radiology.

[32]  F. Dong,et al.  How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10-14 in ultrasound using deep learning , 2022, Comput. Methods Programs Biomed..

[33]  Saleh Albahli,et al.  AI-driven deep convolutional neural networks for chest X-ray pathology identification. , 2022, Journal of X-ray science and technology.

[34]  Vedran Vyroubal,et al.  Classification Efficiency of Pre-Trained Deep CNN Models on Camera Trap Images , 2022, J. Imaging.

[35]  Yunyun Dong,et al.  COVID-19 CT image recognition algorithm based on transformer and CNN , 2022, Displays.

[36]  R. M. Greiwe,et al.  A Validated Algorithm using Current Literature to Judge the Appropriateness of Anatomic Total Shoulder Arthroplasty Utilizing the RAND/UCLA Appropriateness Method. , 2022, Journal of shoulder and elbow surgery.

[37]  Akeem Pedro,et al.  Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies , 2022, International journal of environmental research and public health.

[38]  Chengxu Feng,et al.  The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS , 2021, Sensors.

[39]  Z. Pan,et al.  Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method , 2021, The International Journal of Advanced Manufacturing Technology.

[40]  D. Kitaguchi,et al.  Artificial intelligence‐based computer vision in surgery: Recent advances and future perspectives , 2021, Annals of gastroenterological surgery.

[41]  Christopher Legner,et al.  Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity , 2021, Adv. Intell. Syst..

[42]  C. Reddy,et al.  Attention-based aspect reasoning for knowledge base question answering on clinical notes , 2021, BCB.

[43]  Zenglin Xu,et al.  AFINet: Attentive Feature Integration Networks for Image Classification , 2021, Neural Networks.

[44]  OUP accepted manuscript , 2022, Database.

[45]  Zhennan Huang,et al.  Predicting Ischemic Stroke Outcome Using Deep Learning Approaches , 2022, Frontiers in Genetics.

[46]  J. Straub,et al.  Implementation of Hardware-Based Expert Systems and Comparison of Their Performance to Software-Based Expert Systems , 2021, Machines.

[47]  Jiawei Chen,et al.  Effect of ageing on biochar properties and pollutant management. , 2021, Chemosphere.

[48]  Bingding Huang,et al.  Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks , 2021, Computers in Biology and Medicine.

[49]  Andreas Wichert,et al.  Simple Convolutional-Based Models: Are They Learning the Task or the Data? , 2021, Neural Computation.

[50]  Wensheng Gan,et al.  A Generic Knowledge Based Medical Diagnosis Expert System , 2021, iiWAS.

[51]  V. Denaro,et al.  Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review , 2021, International journal of environmental research and public health.

[52]  Heng Li,et al.  The application of computer vision to visual prosthesis. , 2021, Artificial organs.

[53]  Marek R. Ogiela,et al.  Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications , 2021, Sensors.

[54]  Shailesh Thirumaleshwar,et al.  Artificial Intelligence in Pharmaceutical Field - A Critical Review. , 2021, Current drug delivery.

[55]  Xiaobing Kang,et al.  Review of Weed Detection Methods Based on Computer Vision , 2021, Sensors.

[56]  Aldo von Wangenheim,et al.  What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review , 2021, Comput. Medical Imaging Graph..

[57]  Alva Erwin,et al.  Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF , 2021, Journal of Assisted Reproduction and Genetics.

[58]  Jolande Fooken,et al.  The role of eye movements in manual interception: A mini-review , 2021, Vision Research.

[59]  J. Straub.  Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique , 2021, Knowl. Based Syst..

[60]  Michael A. Chary,et al.  Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence , 2021, Comput. Biol. Medicine.

[61]  Gang Liu,et al.  A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications , 2021, Sensors.

[62]  Jonny Karlsson,et al.  The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation , 2021, Rehabilitation process and outcome.

[63]  Yanning Zhang,et al.  Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN , 2021, Sensors.

[64]  Jan Egger,et al.  A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery , 2020, Expert review of medical devices.

[65]  Chao Niu,et al.  Predicting the evolution of sheet metal surface scratching by the technique of artificial intelligence , 2020, The International Journal of Advanced Manufacturing Technology.

[66]  Liangchi Zhang,et al.  Fuzzy modelling of surface scratching in contact sliding , 2020, IOP Conference Series: Materials Science and Engineering.

[67]  Arthur Francisco Araújo Fernandes,et al.  Image Analysis and Computer Vision Applications in Animal Sciences: An Overview , 2020, Frontiers in Veterinary Science.

[68]  Moncef L Nehdi,et al.  Mitigating Portland Cement CO2 Emissions Using Alkali-Activated Materials: System Dynamics Model , 2020, Materials.

[69]  Chun-Shun Tseng,et al.  Bayesian Edge Detector Using Deformable Directivity-Aware Sampling Window , 2020, Entropy.

[70]  Yong Wang,et al.  Joint Deployment and Task Scheduling Optimization for Large-Scale Mobile Users in Multi-UAV-Enabled Mobile Edge Computing , 2020, IEEE Transactions on Cybernetics.

[71]  Karan Patel,et al.  Assistive device using computer vision and image processing for visually impaired; review and current status , 2020, Disability and rehabilitation. Assistive technology.

[72]  Tuong Manh Vu,et al.  A Software Architecture for Mechanism-Based Social Systems Modelling in Agent-Based Simulation Models , 2020, J. Artif. Soc. Soc. Simul..

[73]  Anthony N. Nguyen,et al.  Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principle , 2020, BMJ Open.

[74]  Mohammad Alshayeb,et al.  Empirical study of the relationship between design patterns and code smells , 2020, PloS one.

[75]  Piotr Gawron,et al.  RA-map: building a state-of-the-art interactive knowledge base for rheumatoid arthritis , 2020, Database J. Biol. Databases Curation.

[76]  Houcemeddine Turki,et al.  Wikidata: A large-scale collaborative ontological medical database , 2019, J. Biomed. Informatics.

[77]  J. Gagne,et al.  Evaluation of Use of Technologies to Facilitate Medical Chart Review , 2019, Drug Safety.

[78]  Wlodzislaw Duch,et al.  supFunSim: Spatial Filtering Toolbox for EEG , 2019, bioRxiv.

[79]  Leonid L. Chepelev,et al.  Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models , 2018, Journal of Digital Imaging.

[80]  Tahar Battikh,et al.  A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis , 2018, Biomed. Signal Process. Control..

[81]  Kunihiko Fukushima,et al.  Margined Winner-Take-All: New learning rule for pattern recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[82]  William W. L. Cheung,et al.  A Fuzzy Logic Expert System to Estimate Intrinsic Extinction Vulnerabilities of Marine Fishes to Fishing , 2004 .