Intelligent Systems and Applications in Computer Vision

Intelligent Systems and Applications in Computer Vision

Detalles Bibliográficos
Otros Autores: Mittal, Nitin (AI innovator), editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Artificial intelligence.
Computational intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869119106719
Tabla de Contenidos:
  • 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.

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