Emerging trends in image processing, computer vision and pattern recognition
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 i...
| Otros Autores: | , , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Waltham, Massachusetts :
Morgan Kaufmann
2015.
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| Edición: | First edition |
| Colección: | Emerging Trends in Computer Science and Applied Computing
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629482106719 |
Tabla de Contenidos:
- 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
