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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 in...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann,
[2015]
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| Edición: | First edition. |
| Colección: | Emerging trends in computer science & applied computing.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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