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Applied Computer Vision through Artificial Intelligence.

Master the cutting-edge field of computer vision and artificial intelligence with this accessible guide to the applications of machine learning and deep learning for real-world solutions in robotics, healthcare, and autonomous systems.

Bibliographic Details
Main Author: Sandhu, Jasminder Kau
Corporate Author: Safari, an O'Reilly Media Company
Other Authors: Sahu, Rakesh, Sandhu, Jasminder Kaur/Kumar, Abhishe
Format: eBook
Language:English
Published: [S.l.] : John Wiley and Sons, Inc.; Wiley-Scrivener, [n.d.]
Edition:1.
Subjects:
Computer vision.
Vision par ordinateur.
Intelligence (AI) & Semantics.
COMPUTERS.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 An Overview of Medical Diagnostics through Artificial Intelligence-Powered Histopathological Imaging and Video Analysis
  • 1.1 Introduction
  • 1.1.1 A Focus on Digital Image and Video Analysis
  • 1.1.2 Overview of Research Article
  • 1.1.2.1 Comparison Between Different Techniques/Comparative Analysis Among the Techniques Available
  • 1.1.2.2 Overview of Data Preprocessing and Meta-Heuristic Algorithms
  • 1.1.3 The Organizational of the Research Article
  • 1.2 Background
  • 1.2.1 Difficulties with Feature Selection
  • 1.3 Preliminaries
  • 1.3.1 Selection of Features (FS)
  • 1.3.2 Classification
  • 1.3.2.1 Support Vector Machine
  • 1.3.2.2 Naïve Bayes
  • 1.3.2.3 ANN
  • 1.3.3 Meta-Heuristic Algorithms in FS
  • 1.3.3.1 Genetic Algorithm
  • 1.3.3.2 Cuckoo Search Optimization
  • 1.3.3.3 BAT Algorithm
  • 1.3.3.4 Grey Wolf Optimizer
  • 1.3.3.5 Harris Hawk Optimization
  • 1.3.3.6 Transition from Exploration to Exploitation
  • 1.4 Experimental Results
  • 1.4.1 Challenges in the Application of a Metaheuristic Algorithm for Classification and Prediction of Medical Disease
  • 1.4.2 Summary of the Review
  • 1.5 Conclusion
  • References
  • Chapter 2 Generative Adversarial Networks: Theory and Application in Synthesis
  • 2.1 Introduction
  • 2.2 Ideologies of GAN
  • 2.3 Architecture of GAN
  • 2.4 Applications of GAN
  • 2.4.1 Image Processing and Computer Vision
  • 2.4.2 Healthcare and Medical Imaging
  • 2.4.3 Natural Language Processing (NLP)
  • 2.4.4 Video and Animation
  • 2.4.5 Gaming and Entertainment
  • 2.4.6 Cybersecurity and Anomaly Detection
  • 2.4.7 Fashion and Retail
  • 2.4.8 Art and Creativity
  • 2.5 Conclusion
  • References
  • Chapter 3 From Pixels to Predictions: Deep Learning for Glaucoma Detection
  • 3.1 Introduction
  • 3.1.1 Glaucoma
  • 3.1.2 Detection of Glaucoma
  • 3.1.3 Deep Learning
  • 3.1.4 Glaucoma Detection Using Deep Learning
  • 3.2 Literature Review
  • 3.2.1 Glaucoma Classification
  • 3.2.2 Glaucoma Detecting
  • 3.3 Problem Statement
  • 3.4 Hybrid Approach for Glaucoma Detection
  • 3.5 Result and Discussion
  • 3.5.1 Confusion Matrix has been Obtained During Testing that is Shown Below for 4 Models
  • 3.6 Conclusion
  • 3.7 Future Scope
  • References
  • Chapter 4 Advancements in Computer Vision for Object Detection and Recognition using DenseNet Deep Learning Model
  • 4.1 Introduction
  • 4.2 Literature Survey
  • 4.2.1 Application of Principles
  • 4.3 Proposed System
  • 4.4 Results and Discussion
  • 4.5 Conclusion
  • References
  • Chapter 5 Deep Learning-Based Detection of Cyber Extortion
  • 5.1 Introduction
  • 5.2 Related Works
  • 5.3 Existing System
  • 5.4 Proposed System
  • 5.5 System Architecture
  • 5.6 Methodology
  • 5.6.1 Data Collection and Preprocessing
  • 5.6.2 Feature Extraction
  • 5.6.3 Voice Processing
  • 5.6.4 Model Architecture