Quantum Inspired Meta-Heuristics for Image Analysis

INTRODUCES QUANTUM INSPIRED TECHNIQUES FOR IMAGE ANALYSIS FOR PURE AND TRUE GRAY SCALE/COLOR IMAGES IN A SINGLE/MULTI-OBJECTIVE ENVIRONMENT

This book will entice readers to design efficient meta-heuristics for image analysis in the quantum domain. It introduces them to the essence of quantum computing paradigm, its features, and properties, and elaborates on the fundamentals of different meta-heuristics and their application to image analysis. As a result, it will pave the way for designing and developing quantum computing inspired meta-heuristics to be applied to image analysis.

Quantum Inspired Meta-heuristics for Image Analysis begins with a brief summary on image segmentation, quantum computing, and optimization. It also highlights a few relevant applications of the quantum based computing algorithms, meta-heuristics approach, and several thresholding algorithms in vogue. Next, it discusses a review of image analysis before moving on to an overview of six popular meta-heuristics and their algorithms and pseudo-codes. Subsequent chapters look at quantum inspired meta-heuristics for bi-level and gray scale multi-level image thresholding; quantum behaved meta-heuristics for true color multi-level image thresholding; and quantum inspired multi-objective algorithms for gray scale multi-level image thresholding. Each chapter concludes with a summary and sample questions.

• Provides an in-depth analysis of quantum mechanical principles
• Offers a comprehensive review of image analysis
• Analyzes different state-of-the-art image thresholding approaches
• Details current, popular standard meta-heuristics in use today
• Guides readers step by step in the build-up of quantum inspired meta-heuristics
• Includes a plethora of real life case studies and applications
• Features statistical test analysis of the performances of the quantum inspired meta-heuristics vis-à-vis their conventional counterparts

Quantum Inspired Meta-heuristics for Image Analysis is an excellent source of information for anyone working with or learning quantum inspired meta-heuristics for image analysis.

本書介紹了在單一/多目標環境中，針對純灰階/彩色圖像進行量子啟發技術的影像分析。本書將引導讀者在量子領域設計高效的元啟發式算法，並介紹量子計算範式的本質、特點和性質，以及不同元啟發式算法的基礎知識和其在影像分析中的應用。因此，本書將為設計和開發基於量子計算的元啟發式算法應用於影像分析鋪平道路。

《量子啟發式影像分析》首先簡要介紹了影像分割、量子計算和優化，並突出了基於量子計算算法、元啟發式方法和幾種流行的閾值算法的一些相關應用。接下來，本書討論了影像分析的綜述，然後概述了六種流行的元啟發式算法及其算法和虛擬碼。隨後的章節探討了基於量子啟發式算法的雙層和灰階多層影像閾值化，以及基於量子行為的真彩多層影像閾值化，還有基於量子啟發式的灰階多層影像閾值化的多目標算法。每章結束時都有一個總結和樣本問題。

本書的特點包括：
- 提供了對量子力學原理的深入分析
- 全面回顧了影像分析
- 分析了不同的最新影像閾值化方法
- 詳細介紹了當今流行的標準元啟發式算法
- 逐步指導讀者建立基於量子啟發式算法
- 包含大量真實案例和應用
- 通過統計測試分析了量子啟發式算法與傳統算法的性能比較

《量子啟發式影像分析》對於任何從事或學習量子啟發式影像分析的人來說都是一個很好的資訊來源。

SANDIP DEY, PHD, is an Associate Professor and Chair in the department of Computer Science & Engineering at the Global Institute of Management and Technology, Krishnanagar, Nadia, West Bengal, India.

SIDDHARTHA BHATTACHARYYA, PHD, is the Principal of RCC Institute of Information Technology, Kolkata, India.

UJJWAL MAULIK, PHD, is the Chair of and Professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India.

SANDIP DEY, PHD，是印度西孟加拉邦納迪亞克里斯納納加爾全球管理和技術學院計算機科學與工程系的副教授和系主任。

SIDDHARTHA BHATTACHARYYA, PHD，是印度加爾各答RCC信息技術學院的校長。

UJJWAL MAULIK, PHD，是印度加爾各答賈達普爾大學計算機科學與工程系的主任和教授。