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دسته بندی: الگوریتم ها و ساختارهای داده ها: پردازش تصویر ویرایش: نویسندگان: Prashant Johri, Mario José Diván, Ruqaiya Khanam, Marcelo Marciszack, Adrián Will سری: EAI/Springer Innovations in Communication and Computing ISBN (شابک) : 303075944X, 9783030759445 ناشر: Springer سال نشر: 2021 تعداد صفحات: 306 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 11 مگابایت
در صورت تبدیل فایل کتاب Trends and Advancements of Image Processing and Its Applications به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب روندها و پیشرفت های پردازش تصویر و کاربردهای آن نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
این کتاب نوآوریها و کاربردهای فناوری کنونی در پردازش تصویر، معرفی تکنیکهای تجزیه و تحلیل و توصیف برنامههای کاربردی در سنجش از دور و ساخت و سایر موارد را پوشش میدهد. نویسندگان مفاهیم جدیدی از دگرگونی فضای رنگ مانند درون یابی رنگ را در میان سایرین گنجانده اند. همچنین مفهوم تبدیل Shearlet و تبدیل موجک و اجرای آنها مورد بحث قرار گرفته است. نویسندگان دیدگاهی درباره مفاهیم و تکنیکهای سنجش از دور مانند استخراج تصویر، منابع جغرافیایی و کشاورزی ارائه میکنند. این کتاب همچنین شامل چندین کاربرد از تجزیه و تحلیل تصویر بیوپزشکی اندام انسان است. علاوه بر این، اصل تشخیص و ردیابی شی متحرک - از جمله روندهای اخیر در وسایل نقلیه متحرک و تشخیص کشتی - توضیح داده شده است. پردازش تصویر؛
This book covers current technological innovations and applications in image processing, introducing analysis techniques and describing applications in remote sensing and manufacturing, among others. The authors include new concepts of color space transformation like color interpolation, among others. Also, the concept of Shearlet Transform and Wavelet Transform and their implementation are discussed. The authors include a perspective about concepts and techniques of remote sensing like image mining, geographical, and agricultural resources. The book also includes several applications of human organ biomedical image analysis. In addition, the principle of moving object detection and tracking ― including recent trends in moving vehicles and ship detection – is described.
Preface Acknowledgments Contents Part I: Recent Trends and Advancements of Image Processing and its Applications Using Convolutional Neural Networks for Classifying COVID-19 in Computerized Tomography Scans 1 Introduction 1.1 Problem Definition 1.2 Formulation of Hypotheses 1.3 Objective Specific Objective 1.4 Methodology 2 Literature Review 2.1 Machine Learning Artificial Neural Networks Neuroscience Inspiration Brief History of Neurocomputing Fundamentals Learning Paradigms 2.2 Deep Learning Convolutional Neural Networks Architecture of Convolutional Neural Networks 2.3 Image Classification Tomography Scans Functional Images PET and Dynamic PET fMRI 2.4 Tools and Techniques TensorFlow Keras NVIDIA CuDNN Transfer Learning Callbacks 3 Experiments 3.1 Environment 3.2 Dataset 3.3 Definition of Models 3.4 Evaluation Metrics 4 Results 5 Conclusion References Challenges in Processing Medical Images in Mobile Devices 1 Introduction 2 Background 2.1 Healthcare and Image Processing 2.2 Distributed Computing 3 Related Work 4 The Platform for Distributed Execution 4.1 Selection 4.2 BOINC 5 Application Execution and Transparency for the Programmer 5.1 The Wrapper for Android OS 5.2 Generation of the Binary Code with the ITK Library 5.3 Parallelization of Image Processing 5.4 Performance Results 6 Proper Use of Devices 6.1 SEAS – Simple Energy-Aware Scheduler 6.2 Integrating the Scheduling Strategy into BOINC 7 Conclusions and Future Work References Smart Traffic Control for Emergency Vehicles Using the Internet of Things and Image Processing 1 Introduction 1.1 Image Processing 1.2 Internet of Things 2 Literature Review 3 Proposed Work 3.1 Traffic Analyzation at Traffic Signal 3.2 To Update a Load of Traffic to the Main Server 3.3 Updating the Node Weights on the Map 3.4 Update the Path Required to Be Followed by an Emergency Vehicle in Real-Time 3.5 Update the IoT Chip about the Emergency Vehicle 3.6 Set the Timer Accordingly 4 Conclusion and Future Work References Combining Image Processing and Artificial Intelligence for Dental Image Analysis: Trends, Challenges, and Applications 1 Introduction 2 An Overview on Digital Dental Imaging 2.1 X-Ray Images 2.2 Intraoral Radiographs 2.3 Extraoral Radiographs 2.4 Computed Tomography and Cone-Beam Computed Tomography 3 Image Processing and Artificial Intelligence for Dental Image Analysis 3.1 Artificial Intelligence Techniques for Image Processing 3.2 Identify Periodontitis 3.3 Detection of Dental Caries 3.4 Image Enhancement 3.5 Other Applications 3.6 Conclusions and Challenge Issues References Median Filter Based on the Entropy of the Color Components of RGB Images 1 Introduction 2 Ordering Strategies 2.1 Ordering Techniques 2.2 Vector Ordering Strategies 3 Proposed Ordering Strategy 3.1 Entropy 3.2 Proposed Filter 4 Experiments and Results 4.1 Mean Absolute Error (MAE) 4.2 Implementation of the Proposal 4.3 Experiments and Results 4.4 Results 5 Conclusions and Future Work References Deep Learning Models for Predicting COVID-19 Using Chest X-Ray Images 1 Introduction 2 Deep Learning 2.1 Convolutional Neural Network 2.2 Residual Network 2.3 Efficient Networks 3 Related Work 4 Methods and Materials 4.1 Dataset 4.2 Data Preparation 4.3 Data Augmentation 4.4 Environment Setup 5 Results and Discussion 6 Performance Evaluation 7 Conclusion References Deep Learning Methods for Chronic Myeloid Leukaemia Diagnosis 1 Scope of Study 1.1 Literature Survey At the National Level At the International Level 2 Introduction 2.1 Why Use Deep Learning 3 Progressive Diagnosis of Chronic Myeloid Leukaemia 3.1 Classification Algorithms Used in CML Diagnosis Support Vector Machines (SVMs) K-Nearest Neighbour (k-NN) Algorithm Naïve Bayes Deep Learning 4 Artificial Neural Networks 5 Convolutional Neural Networks 5.1 Architecture of CNN 6 Typical Application Scenario of Chronic Myeloid Leukaemia 7 Conclusion References An Automatic Bean Classification System Based on Visual Features to Assist the Seed Breeding Process 1 Introduction 2 Related Works 3 Proposed Approach 3.1 Bean Image Acquisition 3.2 Seed Segmentation 3.3 Bean Description and Classification 4 Experimental Setup 5 Experimental Results and Discussion 6 Conclusions References Supervised Machine Learning Classification of Human Sperm Head Based on Morphological Features 1 Introduction 2 Methodology 2.1 Data Acquisition 2.2 Data Preparation 2.3 Sperm Segmentation 2.4 Spermatozoa Abnormality Classification 2.5 Segmentation and Classification Performance Analysis 3 Results and Discussion 3.1 Segmentation 3.2 Sperm Head Morphology Classification 4 Conclusion and Future Work References Future Contribution of Artificial Vision in Methodologies for the Development of Applications That Allow for Identifying Optimal Harvest Times of Medicinal Cannabis Inflorescences in Colombia 1 Introduction 1.1 Inflorescence of Cannabis sp. Maturation and Optimal Harvest Time Generalities About the Inflorescences of Cannabis sp. Plant Structures of Interest Pistils Trichomes Harvest, Maturation, and Harvest Time for Cannabis sp. Inflorescences Types of Maturity Visual and Touch Indicators of Maturation States 1.2 Artificial Intelligence Applied to Image Recognition Multilayer Perceptron Artificial Neural Network (MLP ANN) Convolutional Neural Networks 2 Background on Image Recognition for Similar Applications 3 Methodological Proposal 3.1 Methodology for Capturing Images and Database 3.2 Methodology for the Development of an Application to Recognize the Optimal Harvest Time of the Inflorescences of Cannabis sp. Definition of Requirements Software and System Design 4 Exploration in Image Recognition for Inflorescences of Cannabis sp. Using Artificial Neural Networks 4.1 Construction of the Artificial Neural Network Training Set 4.2 Elaboration and Training of Artificial Neural Networks for Estimating the Maturity State of Cannabis sp. Inflorescences 5 Conclusions and Future Agenda References Detection of Brain Tumor Region in MRI Image Through K-Means Clustering Algorithms 1 Introduction 2 Literature Review 3 Proposed Method 4 K-Means Clustering Algorithm 4.1 Mathematical Representation 4.2 Algorithm 4.3 Pre-processing 4.4 Post-processing Comprises 5 Simulation and Results 6 Conclusion References Estimation of Human Posture Using Convolutional Neural Network Using Web Architecture 1 Introduction 1.1 Pose Estimation 1.2 Pose Estimation Metrics 2 Literature Review 2.1 Background and Techniques 3 Modules of the Proposed System 3.1 Human Pose Extraction Convolutional Neural Network Monitoring of Optical Flow Algorithm of Time Series Data Alignment Description of Framework 4 Conclusion and Future Scope 4.1 Conclusion 4.2 Scope of the Future References Histogram Distance Metric Learning to Diagnose Breast Cancer using Semantic Analysis and Natural Language Interpretation Methods 1 Introduction 2 NLP Levels 2.1 Phonology 2.2 Morphology 2.3 Lexical 2.4 Syntactic 2.5 Semantic 3 Natural Language Generation (NLG) 3.1 Speaker and Generator 3.2 Representation Elements and Rates 3.3 Theological Organization 3.4 Program or Speaker 4 Medicine 5 Breast Cancer 5.1 Categories of Breast Cancer 5.2 Development of Cancer 6 Mammography Screening 6.1 Mammography Diagnosis 6.2 Mammography Online 6.3 Screen Film Mammography 7 Conclusion References Human Skin Color Detection Technique Using Different Color Models 1 Introduction 2 Color Models 2.1 Red, Green, and Blue (RGB) Model 2.2 Hue, Saturation, and Value (HSV) Model 2.3 YCbCr Model 2.4 CMYK (Cyan, Magenta, Yellow, and Black) Model 3 Image Processing and Color Models 3.1 Pseudo-color Image Processing 3.2 Gray Level to Color Transformation Method 3.3 Full-Color Image Processing 3.4 Color Transformation 4 Related Work 5 Proposed Methodology 5.1 Proposed Skin Detection Algorithm 5.2 Restriction of Different Color Spaces 6 Results and Discussion 6.1 Comparative Analysis of Different Color Models 6.2 ROC Analysis 6.3 Experimental Results of Sample Image 7 Conclusion References A Study of Improved Methods on Image Inpainting 1 Introduction 2 Comparative Study 2.1 Image Inpainting 2.2 Sparse Representation 2.3 Markov Random Field Modeling and Multiscale Graph Cuts 2.4 Neural Networks and GAN 2.5 Texture Synthesis 2.6 Feature Distribution 3 Conclusion References Index