The impact of thrust technologies on image processing / Digvijay Pandey [and five others] editors.

Format:

Book

Contributor:

Pandey, Digvijay, editor.

Series:

Technology in a Globalizing World Series

Technology in a globalizing world

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Physical Description:

1 online resource (382 pages)

Edition:

First edition.

Place of Publication:

New York : Nova Science Publishers, Inc., [2023]

Summary:

"Image processing is a method to perform operations on an image to extract information from it or enhance it. Digital image processing has a broad range of applications such as image restoration, medical imaging, remote sensing, image segmentation, etc. Every process requires a different technique. All of this starts with a detailed survey of cutting-edge findings on computational effort, illustrating the functioning of methodologies and highlighting specific purposes for employing specific methodologies, their own impacts on images, and their relevant applications, and offering an audience an overall overview of the discipline and developing a foundation which could indeed be utilized as the basic principle for further study and investigations throughout this field. The content has been made expeditiously and strongly comprehensible and has been demonstrated with various concrete illustrations. This book covers the fundamentals of images as well as image filtration and improvement in the spatial and frequency domains, image reconstruction and restructuring, colour image analysis, wave-lets and other image transforms, image compression and watermarking, morphology, edge detection using conventional methods, segmentation and image feature extraction using cutting-edge technology such as machine learning, deep learning, and IOT"-- Provided by publisher.

Contents:

Intro

Contents

Preface

Acknowledgments

Chapter 1

The Investigation of Image Processing in Forensic Science

Abstract

1. Introduction

1.1. Computer Tools for Image Processing in Forensic Science

1.2. Softwares Used in Forensic Image Processing

1.2.1. Disk and Data Capture Tools

1.2.2. Autopsy/The Sleuth Kit

1.2.3. X-Ways Forensics

1.2.4. Access Data FTK

1.2.5. En Case

1.2.6. Mandiant Red Line

1.2.7. Paraben Suite

1.2.8. Bulk Extractor

1.2.9. Registry Analysis

1.2.10. Registry Recon

1.2.11. Memory Forensics

1.2.12. Volatility

1.2.13. Windows SCOPE

1.2.14. Wireshark

1.2.15. Network Miner

1.2.16. X Plico

2. Image Reconstruction

3. Mobile Device Forensics

3.1. Oxygen Forensic Detective

3.2. Celle Brite UFED

3.3. XRY

3.4. Linux Distros

3.5. CAINE

3.6. SANS SIFT

3.7. HELIX3

4. Processing of Forensic Digital Image

5. Different Types of Digital Image Evidence

6. Use of Digital Image Forensics Techniques

7. Lifecycle of Digital Image

8. Factors Affecting the Digital Image Lifecycle

9. Core Functionalities of Image Forensics Software

10. Forensic Image Analysis

10.1. Photo Image Comparison

10.2. Image Content Analysis (ICE)

10.3. Image Authentication

10.4. Image Enhancement and Restoration

11. Photogrammetry

12. Pros and Cons of Digital Image Forensics

12.1. Pros

12.2. Cons

13. Image Processing for Digital Forensics

Conclusion and Future Outlook

References

Chapter 2

Integrating IoT Based Security with Image Processing

1.1. Internet of Things

1.1.1. Features of IOT

1.1.2. A Comparison between Traditional Internet and IoT

1.1.3. Pros &amp

Cons of IoT

1.1.4. Applications of IoT

1.1.5. Security Flaws of IoT

1.2. Image Processing.

1.2.1. Characteristics of Digital Image Processing (DIP)

1.2.2. Applications of DIP

1.2.3. Architecture/Working

1.3. CBIR

1.3.1. Applications of CBIR

1.4. Camera Surveillance and Machine Vision

1.5. Machine Vision

2. Literature Review

3. Problem Statement

4. Proposed Model

4.1. Role of Canny Edge Detection in Reducing Image Size

5. Results and Discussion

5.1. Comparison of Size during Image Processing in IoT Environment

5.2. Comparison of Time Taken during Image Processing in IoT Environment

Chapter 3

Pattern Analysis for Feature Extraction in Multi-Resolution Images

1.1. Pattern Class

1.2. Analysis

1.3. Pattern Analysis

1.3.1. Pattern Recognition

1.4. Problem Definition

1.5. Pattern Analysis Algorithm

1.6. Feature Extraction

3. Implementation

3.1. Sobel Edge Detector

3.2. Prewitt Edge Detector

3.3. Laplacian Edge Detector

3.4. Canny Edge Detector

Conclusion

Chapter 4

The Design of Microstrip Patch Antenna for 2.4 GHz IoT Based RFID and Image Identification for Smart Vehicle Registry

2. Smart Vehicle Registry

3. Background

4. Design Parameters

4.1. Design Eqation of Inset Feed Microstrip Patch Antenna

4.2. Design of Microstrip

5. Modeling and Analysis

6. Results and Discussion

7. Other Applications

Chapter 5

Kidney Stone Detection from Ultrasound Images Using Masking Techniques

2. Ultrasound Images

3. Contrast Enhancement

4. Proposed Optimum Wavelet Based Masking

4.1. Proposed OWBM Algorithm

5. Cuckoo Search Algorithm

5.1. The Traditional Cuckoo Search Algorithm.

5.2 Need of Adaptive Rebuilding of Worst Nests (ARWN)

5.3. Enhanced Cuckoo Search Algorithm

5.4. Image Segmentation

5.5. Thresholding

Chapter 6

Biometric Technology and Trends

2. History

3. Conventional Biometrics and Modern Age Biometrics Distinguished

4. Biometrics- Trends and Prospects

5. Types of Biometric

6. Trustworthiness and Challenges of Biometrics

7. Challenges and Countermeasures

7.1. Biometric Template Protection

7.2. Error Correction Methods

7.3. Other No Cryptographic Approaches

8. Biometric Technology in Different Spheres of Life

8.1. Commercial Applications

8.2. Law Enforcement and Public Security (Criminal/Suspect Identification)

8.3. Military (Enemy/Ally Identification)

8.4. Border, Travel, and Migration Control (Traveler/Migrant/Passenger Identification)

8.5. Civil Identification (Citizen/Resident/Voter Identification)

8.6. Healthcare and Subsidies (Patient/Beneficiary/Healthcare Professional Identification)

8.7. Physical and Logical Access (Owner/User/Employee/ Contractor/Partner Identification)

8.8. Technological Utilization

8.8.1. Mobile Phones/Tablets

8.8.2. Laptops/PCs

8.8.3. Automobiles

Chapter 7

Comparison of Digital Image Watermarking Methods: An Overview

2. Watermarking

2.1. Classification of Digital Watermarking Techniques

2.1.1. Robust and Fragile Watermarking

2.1.2. Public and Private Watermarking

2.1.3. Asymmetric and Symmetric Watermarking

2.1.4. Steganographic and Non-Steganographic Watermarking

2.1.5. Visible and Invisible Watermarking

2.2. Requirements

2.3. Techniques

2.4. Applications

2.4.1. Copyright Protection.

2.4.2. Copyright Authentication

2.4.3. Fingerprinting and Digital Signatures

2.4.4. Copy Protection and Device Control

2.4.5. Broadcast Monitoring

3. Performance Metrics

3.1. Signal to Noise Ratio (SNR)

3.2. Peak Signal to Noise Ratio (PSNR)

3.3. Weighted Peak Signal to Noise Ratio (WPSNR)

3.4. Effectiveness

3.5. Efficiency

4. Comparison of Watermarking Techniques

Chapter 8

Novel Deep Transfer Learning Models on Medical Images: DINET

1.1. Medical Image Classification: Transfer Learning

3. Methodology

3.1. Datasets

3.2. Deep CNN Phase

3.3. Implementation Details

4. Experiment Results

4.1. Implementation Details

4.2. Experiments

4.3. Evaluation Procedures and Techniques

4.4. Results

5. Discussion

Limitations

Future Outlook

Chapter 9

A Review of the Application of Deep Learning in Image Processing

1.1. Basic Network Structure: Multi-Layer Perception (MLP)

1.2. Convolutional Neural Network (CNN)

2. Network Structure Improvements

2.1. Improvement of Convolutional Neural Network

2.1.1. AlexNet Model

2.1.2. ZFNet Model

2.1.3. Deep Residual Network (ResNet)

2.2. Improvement of Recurrent Neural Network

2.2.1. Long and Short-Term Memory Network (LSTM)

2.2.2. Hierarchical RNN

2.2.3. Bi-Directional RNN

2.2.4. Multi-Dimensional RNN

3. Applications of Deep Learning in Image Processing

3.1. Speech Processing

3.2. Computer Vision

3.3. Natural Language Processing

4. Existing Problems and Future Directions of Deep Learning

4.1. Training Problem

4.1.1. The Gradient Disappearance Problem

4.1.2. Use Large-Scale Labelled Training Datasets.

4.1.3. Distributed Training Problem

4.2. Landing Problem

4.2.1. Too Many Hyper-Parameters

4.2.2. Reliability is Insufficient

4.2.3. Poor Interpretability

4.2.4. Model Size Is Too Large

4.3. Functional Problem

4.3.1. Lack of Ability to Solve Logical Problems

4.3.2. Small Data Challenges

4.3.3. Unable to Handle Multiple Tasks Simultaneously

4.3.4. Ultimate Algorithm

4.4. Domain Issues

4.4.1. Image Understanding Issues

4.4.2. Natural Language Processing Issues

Chapter 10

The Survey and Challenges of Crop Disease Analysis Using Various Deep Learning Techniques

2. Literature Survey

2.1. Leaf Diseases

2.1.1. Grape

2.1.2. Citrus

2.1.3. Apple

2.1.4. Other

3. Four Phase Technique

3.1. Data Collection

3.2. Data Augmentation

3.3. Data Detection and Classification

3.4. Optimization

4. Issues Related to Plant Disease Identification

Chapter 11

Image Processing and Computer Vision: Relevance and Applications in the Modern World

1.1. Digital Image Processing

1.2. Image Acquisition

1.3. Digital Histogram Plots

1.4. Characteristics

2. Image Storage and Manipulation

2.1. Image Segmentation

2.2. Feature Extraction

2.3. Multi-Scale Signal Analysis

2.4. Pattern Recognition

2.5. Projection

3. Image Processing Techniques

3.1. Anisotropic Diffusion

3.2. Hidden Markov Models

3.3. Image Editing

3.4. Image Restoration

3.5. Independent Component Analysis

3.6. Linear Filtering

3.7. Neural Networks

3.8. Point Feature Matching

3.9. Principal Components Analysis

4. Newer Applications of Image Processing

4.1. Active Learning Participation

4.2. Workplace Surveillance

4.3. Building Recognition.

4.4. Image Change Detection.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Pandey, Digvijay The Impact of Thrust Technologies on Image Processing

ISBN:

979-88-86978-52-0