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A Beginner’s Guide to Multilevel Image Thresholding: Intelligent Signal Processing and Data Analysis

Autor Venkatesan Rajinikanth, Nadaradjane Sri Madhava Raja, Nilanjan Dey
en Limba Engleză Paperback – 7 oct 2024
A Beginner’s Guide to Image Multi-Level Thresholding emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement.

  • Explains basic concepts and the implementation of Image Multi-Level Thresholding (grayscale and RGB images)
  • Presents a detailed evaluation in real-time application, including the need for heuristic algorithm, the choice of objective and threshold function, and the evaluation of the outcome
  • Describes how the image thresholding acts as a pre-processing technique and how the region of interest in a medical image is enhanced with thresholding
  • Illustrates integration of the thresholding technique with bio-inspired algorithms
  • Includes current findings and future directions of image multi-level thresholding and its practical implementation
  • Emphasizes the need for multi-level thresholding with suitable examples

The book is aimed at graduate students and researchers in image processing, electronics engineering, computer sciences and engineering.
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Specificații

ISBN-13: 9780367503178
ISBN-10: 0367503174
Pagini: 118
Ilustrații: 66
Dimensiuni: 156 x 234 mm
Greutate: 0.22 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Intelligent Signal Processing and Data Analysis

Locul publicării:Boca Raton, United States

Public țintă

Academic

Cuprins

1. Introduction 2. Thresholding Approaches 3. Grayscale and RGB-scale Image Examination 4. Heuristic Algorithm Assisted Thresholding 5. Objective Function and Image Quality Measures 6. Assessment of Images with Constraints 7. Thresholding of Benchmark Images 8. Thresholding of Biomedical Images 9. Thresholding of Plant-Weed images 10. Conclusion

Descriere

This book emphasizes various image thresholding methods that are necessary for image pre-processing and initial level enhancement. It presents a detailed evaluation, such as choosing the threshold function, need for the heuristic algorithm, choice of objective function, and evaluation of the outcome along with real-time applications in detail.