Emerging trends in image processing, computer vision and pattern recognition / edited by Leonidas Deligiannidis, Hamid R. Arabnia ; contributors, A. Abdel-Dayem [and eighty-eight others].

Format:

Book

Author/Creator:

Deligiannidis, Leonidas, author.

Contributor:

Deligiannidis, Leonidas, editor.

Arabnia, Hamid, editor.

Abdel-Dayem, A., contributor.

Series:

Emerging Trends in Computer Science and Applied Computing

Emerging Trends in Computer Science & Applied Computing

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Optical pattern recognition.

Physical Description:

1 online resource (646 p.)

Edition:

First edition.

Place of Publication:

Waltham, Massachusetts : Morgan Kaufmann, 2015.

Language Note:

English

System Details:

text file

Summary:

Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid

Contents:

Front Cover; Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition; Copyright; Contents; Contributors; Acknowledgments; Preface; Introduction; Part 1: Image and signal processing; Chapter 1: Denoising camera data:Shape-adaptive noise reduction for color filter array image data; 1. Introduction; 2. Camera noise; 3. Adaptive raw data denoising; 3.1. Luminance Transformation of Bayer Data; 3.2. LPA-ICI for Neighborhood Estimation; 3.3. Shape-adaptive DCT and Denoising via Hard Thresholding; 4. Experiments: Image quality vs system performance

4.1. Visual Quality of Denoising Results4.2. Processing Real Camera Data; 5. Video Sequences; 5.1. Implementation Aspects; 6. Conclusion; References; References; References; References; Chapter 2: An approach to classifying four-part music in multidimensional space; 1. Introduction; 1.1. Related Work; 1.2. Explanation of Musical Terms; 2. Collecting the pieces-training and test pieces; 2.1. Downloading and Converting Files; 2.2. Formatting the MusicXML; 3. Parsing musicXML-training and test pieces; 3.1. Reading in Key and Divisions; 3.2. Reading in Notes; 3.3. Handling Note Values

3.4. Results4. Collecting Piece Statistics; 4.1. Metrics; 5. Collecting Classifier Statistics-Training Pieces Only; 5.1. Approach; 6. Classifying Test Pieces; 6.1. Classification Techniques; 6.2. User Interface; 6.3. Classification Steps; 6.4. Testing the Classification Techniques; 6.5. Classifying from Among Two Composers; 6.6. Classifying from Among Three Composers; 6.7. Selecting the Best Metrics; 7. Additional Composer and Metrics; 7.1. Lowell Mason; 7.2. Additional Metrics; 8. Conclusions; Further reading; Chapter 3: Measuring rainbow trout by using simple statistics; 1. Introduction

2. Experimental prototype2.1. Canalization System; 2.2. Illumination System; 2.3. Vision System; 3. Statistical Measuring Approach; 4. Experimental framework; 4.1. Testing Procedure; 5. Performance evaluation; 6. Conclusions; Acknowledgments; Chapter 4: Fringe noise removal of retinal fundus images using trimming regions; 1. Introduction; 1.1. Image Processing; 1.2. Retinal Image Processing; 1.2.1. Ophthalmological Data; 2. Methodology; 2.1. Implementation; 3. Results and Discussion; 4. Conclusion; References; Chapter 5: pSQ: Image quantizer based on contrast band-pass filtering

1. Introduction2. Related Work: JPEG 2000 Global Visual Frequency Weighting; 3. Perceptual quantization; 3.1. Contrast Band-Pass Filtering; 3.2. Forward Inverse Quantization; 3.3. Perceptual Inverse Quantization; 4. Experimental results; 4.1. Based on Histogram; 4.2. Correlation Analysis; 5. Conclusions; Acknowledgments ; References; Chapter 6: Rebuilding IVUS images from raw data of the RF signal exported by IVUS equipment; 1. Introduction; 2. Method for IVUS image reconstruction; 2.1. RF Dataset; 2.2. Band-Pass Filter; 2.3. Time Gain Compensation; 2.4. Signal Envelope; 2.5. Log-Compression

2.6. Digital Development Process

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9780128020920

012802092X

9780128020456

0128020458

OCLC:

900788558