Fundamentals of Image, Audio, and Video Processing Using MATLAB® : With Applications to Pattern Recognition book cover
1st Edition

Fundamentals of Image, Audio, and Video Processing Using MATLAB®
With Applications to Pattern Recognition

  • Available for pre-order. Item will ship after April 16, 2021
ISBN 9780367895242
April 16, 2021 Forthcoming by CRC Press
420 Pages 331 B/W Illustrations

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Book Description

Fundamentals of Image, Audio, and Video Processing Using MATLAB® introduces the concepts and principles of media processing and its applications in pattern recognition by adopting a hands-on approach using program implementations. The book covers the tools and techniques for reading, modifying and writing image, audio and video files using the data analysis and visualization tool MATLAB®.

Key Features:

  • Covers fundamental concepts of image, audio and video processing
  • Demonstrates the use of MATLAB® on solving problems on media processing
  • Discusses important features of Image Processing Toolbox, Audio System Toolbox and Computer Vision Toolbox
  • MATLAB® codes are provided as answers to specific problems
  • Illustrates the use of Simulink for audio and video processing
  • Handles processing techniques in both the Spatio-Temporal domain and Frequency domain.

This is a perfect companion for graduate and post-graduate students studying courses on image processing, speech and language processing, signal processing, video object detection and tracking and related multimedia technologies, with a focus on practical implementations using programming constructs and skill developments. It will also appeal to researchers in the field of pattern recognition, computer vision and content-based retrieval, and for students of MATLAB® courses dealing with media processing, statistical analysis and data visualization.

Table of Contents

Chapter 1: Image Processing

1.1 Introduction

1.2 Tooboxes and Functions

1.2.1 Basic MATLAB (BM) Functions

1.2.2 Image Processing Toolbox (IPT) Functions

1.3 Import Export and Conversions

1.3.1 Read and Write Image Data

1.3.2 Image Type Conversion

1.3.3 Image Color

1.3.4 Synthetic Images

1.4 Display and Exploration

1.4.1 Basic Display

1.4.2 Interactive Exploration

1.4.3 Building Interactive Tools

1.5 Geometric Transformations and Image Registration

1.5.1 Common Geometric Transformations

1.5.2 Affine and Projective Transformations

1.5.3 Image Registration

1.6 Image Filtering and Enhancement

1.6.1 Image Filtering

1.6.2 Edge Detection

1.6.3 Contrast Adjustment

1.6.4 Morphological Operations

1.6.5 ROI and Block Processing

1.6.6 Image Arithmetic

1.6.7 De-blurring

1.7 Image Segmentation and Analysis

1.7.1 Image Segmentation

1.7.2 Object Analysis

1.7.3 Region and Image Properties

1.7.4 Texture Analysis

1.7.5 Image Quality

1.7.6 Image Transforms

1.8 Working in Frequency Domain

1.9 Image Processing using Simulink

1.10 Notes on 2-D Plotting Functions

1.11 Notes on 3-D Plotting Functions

Review Questions


Chapter 2: Audio Processing

2.1 Introduction

2.2 Tooboxes and Functions

2.2.1 Basic MATLAB (BM) Functions

2.2.2 Audio System Toolbox (AST) Functions

2.2.3 Other Toolbox Functions

2.3 Sound Waves

2.4 Audio I/O and Waveform Generation

2.5 Audio Processing Algorithm Design

2.6 Measurements and Feature Extraction

2.7 Simulation Tuning and Visualization

2.8 Musical Instrument Digital Interface (MIDI)

2.9 Temporal Filters

2.10 Spectral Filters

2.11 Audio Processing using Simulink

Review Questions


Chapter 3: Video Processing

 3.1 Introduction

3.2 Tooboxes and Functions

3.2.1 Basic MATLAB (BM) Functions

3.2.2 Computer Vision System Toolbox (CVST) Functions

3.3 Video Input Output and Playback

3.4 Processing Video Frames

3.5 Video Color Spaces

3.6 Object Detection

3.6.1 Blob Detector

3.6.2 Foreground Detector

3.6.3 People Detector

3.6.4 Face Detector

3.6.5 Optical Character Recognition

3.7 Motion Tracking

3.7.1 Histogram Based Tracker

3.7.2 Optical Flow

3.7.3 Point Tracker

3.7.4 Kalman Filter

3.7.5 Block Matcher

3.8 Video Processing using Simulink

Review Questions


Chapter 4: Pattern Recognition

4.1 Introduction

4.2 Tooboxes and Functions

4.2.1 Computer Vision System Toolbox (CVST)

4.2.2 Statistics and Machine Learning Toolbox (SMLT)

4.3 Data Acquisition

4.4 Pre-Processing

4.5 Feature Extraction

4.5.1 Minimum Eigenvalue Algorithm

4.5.2 Harris Corner Detection Algorithm

4.5.3 FAST Algorithm

4.5.4 MSER Algorithm

4.5.5 SURF Algorithm

4.5.6 KAZE Algorithm

4.5.7 BRISK Algorithm

4.5.8 LBP Algorithm

4.5.9 HOG Algorithm

4.6 Clustering

4.6.1 Similarity Metrics

4.6.2 -means clustering

4.6.3 Hierarchical clustering

4.6.4 GMM based clustering

4.7 Classification

4.7.1 -NN classifiers

4.7.2 Artificial Neural Network (ANN) classifiers

4.7.3 Decision Tree classifiers

4.7.4 Discriminant Analysis classifiers

4.7.5 Naive Bayes classifiers

4.7.6 Support Vector Machine (SVM) classifiers

4.7.7 Classification Learner App

 4.8 Performance Evaluation

Review Questions

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Dr. Ranjan Parekh, PhD (Engineering), is Professor at the School of Education Technology, Jadavpur University, Calcutta, India, and is involved with teaching subjects related to Graphics and Multimedia at the post graduate level. His research interests include multimedia information processing, pattern recognition, and computer vision.