| Description |
1 online resource (237 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Content -- Preface -- Acknowledgments -- Authors -- Part I: Concept and Background of Image Processing, Techniques, and Big Data -- Chapter 1: Introduction to Advanced Digital Image Processing -- 1.1 Introduction -- 1.2 Categorization of Digital Images -- 1.2.1 Binary Image -- 1.2.2 Black and White Image -- 1.2.3 8-Bit Color Format -- 1.2.4 16 Color Format -- 1.2.5 24-Bit Format -- 1.3 Phases of Digital Image Processing -- 1.3.1 Acquisition of an Image -- 1.3.2 Image Enhancement -- References |
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Chapter 2: Different Techniques Used for Image Processing -- 2.1 Introduction -- 2.1.1 Acquisition of an Image -- 2.1.2 Image Pre-Processing -- 2.1.2.1 Image Enhancement -- 2.1.2.2 Image Analysis -- 2.1.2.3 Image Compression -- 2.1.2.4 Edge Detection -- 2.1.2.5 Segmentation -- 2.1.2.6 Image Representation -- References -- Chapter 3: Role and Support of Image Processing in Big Data -- 3.1 Introduction -- 3.2 Big Data Mathematical Analysis Theories -- 3.2.1 Independent and Identical Distribution Theory (IID) -- 3.2.2 Set Theory -- 3.3 Characteristics of Big Data |
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3.4 Different Techniques of Big Data Analytics -- 3.4.1 Ensemble Analysis -- 3.4.2 Association Analysis -- 3.4.3 High-Dimensional Analysis -- 3.4.4 Deep Analysis -- 3.4.5 Precision Analysis -- 3.4.6 Divide and Conquer Analysis -- 3.4.7 Perspective Analysis -- 3.5 Steps of Big Data Processing -- 3.5.1 Data Collection -- 3.5.2 Data Storage and Management -- 3.5.3 Data Filtering and Extraction -- 3.5.4 Data Cleaning and Validation -- 3.5.5 Data Analytics -- 3.5.6 Data Visualization -- 3.6 Importance of Big Data in Image Processing -- 3.7 Hadoop -- 3.8 Parts of Hadoop Architecture -- 3.8.1 HDFS |
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3.8.2 Map Reduce -- 3.9 Working of HADOOP architecture -- 3.10 Image Processing with Big Data Analytics -- 3.11 Image preprocessing -- References -- Part II: Advanced Image Processing Technical Phases for Big Data Analysis -- Chapter 4: Advanced Image Segmentation Techniques Used for Big Data -- 4.1 Introduction -- 4.2 Classification of Image Segmentation Techniques -- 4.2.1 Region-based Segmentation -- 4.2.1.1 Threshold Segmentation -- 4.2.1.2 Regional Growth Segmentation -- 4.2.1.3 Region Splitting and Merging Methods -- 4.2.2 Edge Detection Segmentation -- 4.2.2.1 Sobel Operator |
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4.2.2.2 Laplacian Operator -- 4.2.3 Clustering-Based Segmentation -- 4.2.3.1 Hard Clustering -- 4.2.3.2 Soft Clustering -- 4.2.3.3 K-Means Clustering Technique -- 4.2.3.4 Fuzzy C-Means Clustering Technique -- 4.2.4 Segmentation Based on Weakly Supervised Learning in CNN -- 4.2.4.1 Comparative Study of Image Segmentation Techniques -- 4.3 Discussion -- References -- Chapter 5: Advance Object Detection and Clustering Techniques Used for Big Data -- 5.1 Introduction -- 5.2 Clustering -- 5.3 Differences between Clustering and Classification -- 5.4 Distance Measure -- 5.4.1 Euclidean Distance |
| Notes |
Description based upon print version of record |
| Form |
Electronic book
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| Author |
Ashok, Alaknanda
|
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Verma, Parag
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Verma, Poonam
|
| ISBN |
9781000281392 |
|
1000281396 |
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