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Fundamentals of Image, Audio, and Video Processing Using MATLAB®: With Applications to Pattern Recognition [Mīkstie vāki]

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  • Formāts: Paperback / softback, 388 pages, height x width: 254x178 mm, weight: 750 g, 254 Line drawings, black and white; 77 Halftones, black and white; 331 Illustrations, black and white
  • Izdošanas datums: 01-Oct-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 0367748347
  • ISBN-13: 9780367748340
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  • Cena: 62,51 €
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Fundamentals of Image, Audio, and Video Processing Using MATLAB®: With Applications to Pattern RecognitionPalielināt
  • Formāts: Paperback / softback, 388 pages, height x width: 254x178 mm, weight: 750 g, 254 Line drawings, black and white; 77 Halftones, black and white; 331 Illustrations, black and white
  • Izdošanas datums: 01-Oct-2022
  • Izdevniecība: CRC Press
  • ISBN-10: 0367748347
  • ISBN-13: 9780367748340
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367748340.html
Keywords:
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.

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 interest includes multimedia information processing, pattern recognition, and computer vision.
Preface ix
Author xv
Abbreviations xvii
1 Image Processing
1(190)
1.1 Introduction
1(4)
1.2 Toolboxes and Functions
5(9)
1.2.1 Basic MATLAB® (BM) Functions
6(3)
1.2.2 Image Processing Toolbox (IPT) Functions
9(4)
1.2.3 Signal Processing Toolbox (SPT) Functions
13(1)
1.2.4 Wavelet Toolbox (WT) Functions
13(1)
1.3 Import Export and Conversions
14(36)
1.3.1 Read and Write Image Data
14(2)
1.3.2 Image-Type Conversion
16(15)
1.3.3 Image Color
31(13)
1.3.4 Synthetic Images
44(6)
1.4 Display and Exploration
50(8)
1.4.1 Basic Display
50(4)
1.4.2 Interactive Exploration
54(3)
1.4.3 Building Interactive Tools
57(1)
1.5 Geometric Transformation and Image Registration
58(15)
1.5.1 Common Geometric Transformations
58(6)
1.5.2 Affine and Projective Transformations
64(3)
1.5.3 Image Registration
67(6)
1.6 Image Filtering and Enhancement
73(36)
1.6.1 Image Filtering
73(7)
1.6.2 Edge Detection
80(6)
1.6.3 Contrast Adjustment
86(6)
1.6.4 Morphological Operations
92(3)
1.6.5 ROI and Block Processing
95(4)
1.6.6 Image Arithmetic
99(2)
1.6.7 De-blurring
101(8)
1.7 Image Segmentation and Analysis
109(35)
1.7.1 Image Segmentation
109(2)
1.7.2 Object Analysis
111(7)
1.7.3 Region and Image Properties
118(7)
1.7.4 Texture Analysis
125(4)
1.7.5 Image Quality
129(2)
1.7.6 Image Transforms
131(13)
1.8 Working in Frequency Domain
144(5)
1.9 Image Processing Using Simulink
149(6)
1.10 Notes on 2-D Plotting Functions
155(27)
1.11 Notes on 3-D Plotting Functions
182(9)
Review Questions
190(1)
2 Audio Processing
191(68)
2.1 Introduction
191(2)
2.2 Toolboxes and Functions
193(4)
2.2.1 Basic MATLAB® (BM) Functions
193(2)
2.2.2 Audio System Toolbox (AST) Functions
195(1)
2.2.3 DSP System Toolbox (DSPST) Functions
196(1)
2.2.4 Signal Processing Toolbox (SPT) Functions
196(1)
2.3 Sound Waves
197(13)
2.4 Audio I/O and Waveform Generation
210(5)
2.5 Audio Processing Algorithm Design
215(10)
2.6 Measurements and Feature Extraction
225(6)
2.7 Simulation, Tuning and Visualization
231(4)
2.8 Musical Instrument Digital Interface (MIDI)
235(2)
2.9 Temporal Filters
237(4)
2.10 Spectral Filters
241(13)
2.11 Audio Processing Using Simulink
254(5)
Review Questions
257(2)
3 Video Processing
259(44)
3.1 Introduction
259(3)
3.2 Toolboxes and Functions
262(2)
3.2.1 Basic MATLAB® (BM) Functions
262(1)
3.2.2 Computer Vision System Toolbox (CVST) Functions
263(1)
3.3 Video Input Output and Playback
264(8)
3.4 Processing Video Frames
272(6)
3.5 Video Color Spaces
278(4)
3.6 Object Detection
282(7)
3.6.1 Blob Detector
282(2)
3.6.2 Foreground Detector
284(1)
3.6.3 People Detector
285(1)
3.6.4 Face Detector
286(2)
3.6.5 Optical Character Recognition (OCR)
288(1)
3.7 Motion Tracking
289(8)
3.7.1 Histogram Based Tracker
289(2)
3.7.2 Optical Flow
291(2)
3.7.3 Point Tracker
293(1)
3.7.4 Kalman Filter
294(2)
3.7.5 Block Matcher
296(1)
3.8 Video Processing Using Simulink
297(6)
Review Questions
300(3)
4 Pattern Recognition
303(68)
4.1 Introduction
303(1)
4.2 Toolboxes and Functions
304(2)
4.2.1 Computer Vision System Toolbox (CVST)
304(1)
4.2.2 Statistics and Machine Learning Toolbox (SMLT)
305(1)
4.2.3 Neural Network Toolbox (NNT)
306(1)
4.3 Data Acquisition
306(5)
4.4 Pre-processing
311(1)
4.5 Feature Extraction
312(12)
4.5.1 Minimum Eigenvalue Method
312(2)
4.5.2 Harris Corner Detector
314(1)
4.5.3 FAST Algorithm
315(1)
4.5.4 MSER Algorithm
316(1)
4.5.5 SURF Algorithm
317(3)
4.5.6 KAZE Algorithm
320(1)
4.5.7 BRISK Algorithm
321(1)
4.5.8 LBP Algorithm
322(2)
4.5.9 HOG Algorithm
324(1)
4.6 Clustering
324(13)
4.6.1 Similarity Metrics
324(4)
4.6.2 k-means Clustering
328(4)
4.6.3 Hierarchical Clustering
332(3)
4.6.4 GMM-Based Clustering
335(2)
4.7 Classification
337(28)
4.7.1 k-NN Classifiers
338(1)
4.7.2 Artificial Neural Network (ANN) classifiers
339(7)
4.7.3 Decision Tree Classifiers
346(2)
4.7.4 Discriminant Analysis Classifiers
348(5)
4.7.5 Naive Bayes Classifiers
353(1)
4.7.6 Support Vector Machine (SVM) Classifiers
354(2)
4.7.7 Classification Learner App
356(9)
4.8 Performance Evaluation
365(6)
Review Questions
369(2)
Function Summary 371(10)
References 381(4)
Index 385
Dr. Ranjan Parekh, PhD (Engineering), is former 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.