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Book Description

This edited book brings together leading researchers, academic scientists and research scholars to put forward and share their experiences and research results on all aspects of an inspection system for detection analysis for various machine vision applications. It also provides a premier interdisciplinary platform to present and discuss the most recent innovations, trends, methodology, applications, and concerns as well as practical challenges encountered and solutions adopted in the inspection system in terms of image processing and analytics of machine vision for real and industrial application.

Machine vision inspection systems (MVIS) utilized all industrial and non-industrial applications where the execution of their utilities based on the acquisition and processing of images. MVIS can be applicable in industry, governmental, defense, aerospace, remote sensing, medical, and academic/education applications but constraints are different. MVIS entails acceptable accuracy, high reliability, high robustness, and low cost. Image processing is a well-defined transformation between human vision and image digitization, and their techniques are the foremost way to experiment in the MVIS. The digital image technique furnishes improved pictorial information by processing the image data through machine vision perception. Digital image pro­cessing has widely been used in MVIS applications and it can be employed to a wide diversity of problems particularly in Non-Destructive testing (NDT), presence/absence detection, defect/fault detection (weld, textile, tiles, wood, etc.,), automated vision test & measurement, pattern matching, optical character recognition & verification (OCR/OCV), barcode reading and traceability, medical diagnosis, weather forecasting, face recognition, defence and space research, etc. This edited book is designed to address various aspects of recent methodologies, concepts and research plan out to the readers for giving more depth insights for perusing research on machine vision using image processing techniques.

Table of Contents

  1. Cover
  2. Preface
  3. 1 Land-Use Classification with Integrated Data
    1. 1.1 Introduction
    2. 1.2 Background Study
    3. 1.3 System Design
    4. 1.4 Implementation Details
    5. 1.5 System Evaluation
    6. 1.6 Discussion
    7. 1.7 Conclusion
    8. References
  4. 2 Indian Sign Language Recognition Using Soft Computing Techniques
    1. 2.1 Introduction
    2. 2.2 Related Works
    3. 2.3 Experiments
    4. 2.4 Summary
    5. References
  5. 3 Stored Grain Pest Identification Using an Unmanned Aerial Vehicle (UAV)-Assisted Pest Detection Model
    1. 3.1 Introduction
    2. 3.2 Related Work
    3. 3.3 Proposed Model
    4. 3.4 Results and Discussion
    5. 3.5 Conclusion
    6. References
  6. 4 Object Descriptor for Machine Vision
    1. 4.1 Outline
    2. 4.2 Chain Codes
    3. 4.3 Polygonal Approximation
    4. 4.4 Moments
    5. 4.5 HU Invariant Moments
    6. 4.6 Zernike Moments
    7. 4.7 Fourier Descriptors
    8. 4.8 Quadtree
    9. 4.9 Conclusion
    10. References
  7. 5 Flood Disaster Management
    1. 5.1 Flood Management
    2. 5.2 Existing Disaster Management Systems
    3. 5.3 Advancements in Disaster Management Technologies
    4. 5.4 Proposed System
    5. References
  8. 6 Temporal Color Analysis of Avocado Dip for Quality Control
    1. 6.1 Introduction
    2. 6.2 Materials and Methods
    3. 6.3 Image Acquisition
    4. 6.4 Image Processing
    5. 6.5 Experimental Design
    6. 6.6 Results and Discussion
    7. 6.7 Conclusion
    8. References
  9. 7 Image and Video Processing for Defect Detection in Key Infrastructure
    1. 7.1 Introduction
    2. 7.2 Reasons for Defective Roads and Bridges
    3. 7.3 Image Processing for Defect Detection
    4. 7.4 Image-Based Defect Detection Methods
    5. 7.5 Factors Affecting the Performance
    6. 7.6 Achievements and Issues
    7. 7.7 Conclusion
    8. References
  10. 8 Methodology for the Detection of Asymptomatic Diabetic Retinopathy
    1. 8.1 Introduction
    2. 8.2 Key Steps of Computer-Aided Diagnostic Methods
    3. 8.3 DR Screening and Grading Methods
    4. 8.4 Key Observations from Literature Review
    5. 8.5 Design of Experimental Methodology
    6. 8.6 Conclusion
    7. References
  11. 9 Offline Handwritten Numeral Recognition Using Convolution Neural Network
    1. 9.1 Introduction
    2. 9.2 Related Work Done
    3. 9.3 Data Set Used for Simulation
    4. 9.4 Proposed Model
    5. 9.5 Result Analysis
    6. 9.6 Conclusion and Future Work
    7. References
  12. 10 A Review on Phishing—Machine Vision and Learning Approaches
    1. 10.1 Introduction
    2. 10.2 Literature Survey
    3. 10.3 Role of Data Mining in Antiphishing
    4. 10.4 Conclusion
    5. Acknowledgments
    6. References
  13. Index
  14. End User License Agreement
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