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ویرایش: نویسندگان: Nitin Mittal, Amit Kant Pandit, Mohamed Abouhawwash, Shubham Mahajan سری: ISBN (شابک) : 9781003453406 ناشر: CRC Press سال نشر: 2024 تعداد صفحات: 341 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 42 Mb
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در صورت تبدیل فایل کتاب Intelligent Systems and Applications in Computer Vision 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 سیستم های هوشمند و برنامه های کاربردی در بینایی کامپیوتر 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.
Cover Half Title Title Page Copyright Page Table of Contents About the Editors List of Contributors Chapter 1 A Review Approach On Deep Learning Algorithms in Computer Vision 1.1 Introduction 1.2 Deep Learning Algorithms 1.2.1 Convolutional Neural Networks 1.2.2 Restricted Boltzmann Machines 1.2.3 Deep Boltzmann Machines 1.2.4 Deep Belief Networks 1.2.5 Stacked (de-Noising) Auto-Encoders 1.2.5.1 Auto-Encoders 1.2.5.2 Denoising Auto Encoders 1.3 Comparison of the Deep Learning Algorithms 1.4 Challenges in Deep Learning Algorithms 1.5 Conclusion and Future Scope References Chapter 2 Object Extraction From Real Time Color Images Using Edge Based Approach 2.1 Introduction 2.2 Applications of Object Extraction 2.3 Edge Detection Techniques 2.3.1 Roberts Edge Detection 2.3.2 Sobel Edge Detection 2.3.3 Prewitt’s Operator 2.3.4 Laplacian Edge Detection 2.4 Related Work 2.5 Proposed Model 2.6 Results and Discussion 2.7 Conclusion References Chapter 3 Deep Learning Techniques for Image Captioning 3.1 Introduction to Image Captioning 3.1.1 How Does Image Recognition Work? 3.2 Introduction to Deep Learning 3.2.1 Pros of the Deep Learning Algorithm 3.2.2 Customary / Traditional CV Methodology 3.2.3 Limitations/challenges of Traditional CV Methodology 3.2.4 Overcome the Limitations of Deep Learning 3.3 Deep Learning Algorithms for Object Detection 3.3.1 Types of Deep Models for Object Detection 3.4 How Image Captioning Works 3.4.1 Transformer Based Image Captioning 3.4.2 Visual Scene Graph Based Image Captioning 3.4.3 Challenges in Image Captioning 3.5 Conclusion References Chapter 4 Deep Learning-Based Object Detection for Computer Vision Tasks: A Survey of Methods and Applications 4.1 Introduction 4.2 Object Detection 4.3 Two-Stage Object Detectors 4.3.1 R-CNN 4.3.2 SPPNet 4.3.3 Fast RCNN 4.3.4 Faster RCNN 4.3.5 R-FCN 4.3.6 FPN 4.3.7 Mask RCNN 4.3.8 G-RCNN 4.4 One-Stage Object Detectors 4.4.1 YOLO 4.4.2 CenterNet 4.4.3 SSD 4.4.4 RetinaNet 4.4.5 EfficientDet 4.4.6 YOLOR 4.5 Discussion On Model Performance 4.5.1 Future Trends 4.6 Conclusion References Chapter 5 Deep Learning Algorithms for Computer Vision: A Deep Insight Into Principles and Applications 5.1 Introduction 5.2 Preliminary Concepts of Deep Learning 5.2.1 Artificial Neural Network 5.2.2 Convolution Neural Network (CNNs) 5.3 Recurrent Neural Network (RNNs) 5.4 Overview of Applied Deep Learning in Computer Vision 5.6 Industrial Applications of Computer Vision 5.7 Future Scope in Computer Vision 5.8 Conclusion References Chapter 6 Handwritten Equation Solver Using Convolutional Neural Network 6.1 Introduction 6.2 State-Of-The-Art 6.3 Convolutional Neural Network 6.3.1 Convolution Layer 6.3.2 Pooling Layer 6.3.3 Fully Connected Layer 6.3.4 Activation Function 6.4 Handwritten Equation Recognition 6.4.1 Dataset Preparation 6.4.2 Proposed Methodology 6.4.2.1 Dataset Acquisition 6.4.2.2 Preprocessing 6.4.2.3 Recognition Through CNN Model 6.4.2.4 Processing Inside CNN Model 6.4.3 Solution Approach 6.5 Results and Discussion 6.6 Conclusion and Future Scope References Chapter 7 Agriware: Crop Suggester System By Estimating the Soil Nutrient Indicators 7.1 Introduction 7.2 Related Work 7.3 Proposed Methodology 7.4 Experimental Results and Discussion 7.5 Conclusion and Future Work References Chapter 8 A Machine Learning Based Expeditious Covid-19 Prediction Model Through Clinical Blood Investigations 8.1 Introduction 8.2 Literature Survey 8.3 Methodology 8.3.1 Dataset and Its Preparation 8.3.2 Classification Set Up 8.3.3 Performance Evaluation 8.4 Results and Discussion 8.5 Conclusion References Chapter 9 Comparison of Image Based and Audio Based Techniques for Bird Species Identification 9.1 Introduction 9.2 Literature Survey 9.3 Methodology 9.4 System Design 9.4.1 Dataset Used 9.4.2 Image Based Techniques 9.4.3 Audio Based Techniques 9.5 Results and Analysis 9.6 Conclusion References Chapter 10 Detection of Ichthyosis Vulgaris Using SVM 10.1 Introduction 10.2 Literature Survey 10.3 Types of Ichthyosis 10.3.1 Ichthyosis Vulgaris 10.3.2 Hyperkeratosis 10.4 Sex-Connected Ichthyosis 10.5 Symptoms 10.6 Complications 10.7 Diagnosis 10.8 Methodology 10.9 Results 10.10 Future Work 10.11 Conclusion References Chapter 11 Chest X-Ray Diagnosis and Report Generation: Deep Learning Approach 11.1 Introduction 11.2 Literature Review 11.3 Proposed Methodology 11.3.1 Overview of Deep Learning Algorithms 11.3.2 Data 11.3.3 Feature Extraction 11.3.4 Report Generation 11.3.5 Evaluation Metrics 11.4 Results and Discussions 11.4.1 Feature Extraction 11.4.2 Report Generation 11.5 Conclusion References Chapter 12 Deep Learning Based Automatic Image Caption Generation for Visually Impaired People 12.1 Introduction 12.2 Related Work 12.3 Methods and Materials 12.3.1 Data Set 12.3.2 Deep Neural Network Architectures 12.3.2.1 Convolution Neural Networks (CNNs) 12.3.2.2 Long Short-Term Memory (LSTM) 12.3.3 Proposed Model 12.3.3.1 Feature Extraction Models 12.3.3.2 Workflow for Image Caption Generation 12.4 Results and Discussion 12.4.1 Evaluation Metrics 12.4.2 Analysis of Results 12.4.3 Examples 12.5 Discussion and Future Work 12.6 Conclusions References Chapter 13 Empirical Analysis of Machine Learning Techniques Under Class Imbalance and Incomplete Datasets 13.1 Introduction 13.2 Related Work 13.2.1 Class Imbalance 13.2.2 Missing Values 13.2.3 Missing Value in Class Imbalance Datasets 13.3 Methodology 13.4 Results 13.4.1 Overall Performance 13.4.2 Effect of Class Imbalance and Missing Values 13.5 Conclusion References Chapter 14 Gabor Filter as Feature Extractor in Anomaly Detection From Radiology Images 14.1 Introduction 14.2 Literature Review 14.3 Research Methodology 14.3.1 Data Set 14.3.2 Gabor Filter 14.4 Results 14.5 Discussion 14.6 Conclusion References Chapter 15 Discriminative Features Selection From Zernike Moments for Shape Based Image Retrieval System 15.1 Introduction 15.2 Zernike Moments Descriptor (ZMD) 15.2.1 Zernike Moments (ZMs) 15.2.2 Orthogonality 15.2.3 Rotation Invariance 15.2.4 Features Selection 15.3 Discriminative Features Selection 15.4 Similarity Measure 15.5 Experimental Study 15.5.1 Experiment Setup 15.5.2 Performance Measurement 15.5.3 Experiment Results 15.6 Discussions and Conclusions References Chapter 16 Corrected Components of Zernike Moments for Improved Content Based Image Retrieval: A Comprehensive Study 16.1 Introduction 16.2 Proposed Descriptors 16.2.1 Invariant Region Based Descriptor Using Corrected ZMs Features 16.2.2 Selection of Appropriate Features 16.2.3 Invariant Contour Based Descriptor Using HT 16.3 Similarity Metrics 16.4 Experimental Study and Performance Evaluation 16.4.1 Measurement of Retrieval Accuracy 16.4.2 Performance Comparison and Experiment Results 16.5 Discussion and Conclusion References Chapter 17 Translate and Recreate Text in an Image 17.1 Introduction 17.2 Literature Survey 17.3 Existing System 17.4 Proposed System 17.4.1 Flow Chart 17.4.2 Experimental Setup 17.4.3 Dataset 17.4.4 Text Detection and Extraction 17.4.5 Auto Spelling Correction 17.4.6 Machine Translation and Inpainting 17.5 Implementation 17.5.1 Text Detection and Extraction 17.5.2 Auto Spelling Correction 17.5.2.1 Simple RNN 17.5.2.2 Embed RNN 17.5.2.3 Bidirectional LSTM 17.5.2.4 Encoder Decoder With LSTM 17.5.2.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance 17.5.3 Machine Translation 17.5.4 Inpainting 17.6 Result Analysis 17.6.1 Simple RNN 17.6.2 Embed RNN 17.6.3 Bidirectional LSTM 17.6.4 Encoder Decoder With LSTM 17.6.5 Encoder Decoder With Bidirectional LSTM + Levenshtein Distance 17.6.6 BLEU (Bilingual Evaluation Understudy) 17.7 Conclusion Acknowledgments References Chapter 18 Multi-Label Indian Scene Text Language Identification: Benchmark Dataset and Deep Ensemble Baseline 18.1 Introduction 18.2 Related Works 18.3 IIITG-MLRIT2022 18.4 Proposed Methodology 18.4.1 Transfer Learning 18.4.1.1 ResNet50 [37] 18.4.1.2 XceptionNet [39] 18.4.1.3 DenseNet [38] 18.4.1.4 MobileNetV2 [36] 18.4.2 Convolutional Neural Network 18.4.3 Multi-Label Deep Ensemble Via Majority Voting 18.4.4 Weighted Binary Cross Entropy 18.5 Training and Experiment 18.6 Results and Discussion 18.7 Conclusion References Chapter 19 AI Based Wearables for Healthcare Applications: A Survey of Smart Watches 19.1 Introduction 19.2 Systematic Review 19.2.1 Criterion to Select Research 19.2.2 Source of Information 19.2.2.1 Search Plan 19.2.2.2 Data Abstraction 19.2.3 Outcomes 19.2.4 Healthcare Applications 19.2.4.1 Activity and Human Motion 19.2.4.2 Healthcare Education 19.2.5 Ideal Smart watch Characteristics 19.2.5.1 Operating System 19.2.5.2 Sensors 19.3 Discussion 19.4 Concluding Remarks References Chapter 20 Nature Inspired Computing for Optimization 20.1 Introduction 20.2 Components of Nature-Inspired Computing 20.2.1 Fuzzy Logic Based Computing 20.2.2 Artificial Neural Networks 20.2.3 Search and Optimization Approaches 20.2.3.1 Evolutionary Computing 20.3 Swarm Intelligence 20.3.1 Particle Swarm Optimization (PSO) 20.3.2 Ant Colony Optimization (ACO) 20.3.3 Artificial Bee Colony (ABC) 20.4 Physics Or Chemistry-Based Search and Optimization Approaches 20.4.1 Intelligent Water Drops Algorithm (IWD) 20.4.2 EM (Electromagnetism-Like Mechanism) Algorithm 20.4.3 Gravitational Search Algorithm (GSA) 20.5 Conclusion References Chapter 21 Automated Smart Billing Cart for Fruits 21.1 Introduction 21.2 Literature Survey 21.3 Proposed Method 21.3.1 System Design 21.4 Implementation 21.5 Results and Discussions 21.6 Results 21.7 Conclusion References Index