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Modern algorithms for image processing : computer imagery by example using C# /
Utilize modern methods for digital image processing and take advantage of the many time-saving templates provided for all of the projects included in this book. Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York, New York] :
[Apress],
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro; Table of Contents; About the Author; Acknowledgments; Introduction; Part I: Image Processing; Chapter 1: Introduction; Chapter 2: Noise Reduction; The Simplest Filter; The Simplest Averaging Filter; The Fast Averaging Filter; The Fast Gaussian Filter; The Median Filter; Sigma Filter: The Most Efficient One; Suppression of Impulse Noise; Chapter 3: Contrast Enhancement; Automatic Linear Contrast Enhancement; Histogram Equalization; Measuring the Lightness of Color Images; Contrast of Color Images; Manually Controlled Contrast Enhancement; Chapter 4: Shading Correction with Thresholding
- Thresholding the ImagesChapter 5: Project WFshadBinImpulse; Part II: Image Analysis; Chapter 6: Edge Detection; Laplacian Operator; The Method of Zero Crossing; Are Zero Crossings of Laplacian Closed Curves?; How to Eliminate Irrelevant Crossings; Noise Reduction Before Using the Laplacian; Blur During the Digitization and Extreme Value Filter; Fundamental Errors of the Method of Zero Crossing in the Laplacian; Chapter 7: A New Method of Edge Detection; Means for Encoding the Edges; The Idea of an Abstract Cell Complex; A Simple Method of Encoding Edges
- Improvements of the Method of Binarized GradientFurther Improvements of the Method of Binarized Gradient; The Edge Detector of Canny; Edges in Color Images; Conclusions; Chapter 8: A New Method of Image Compression; Using a Cell Complex for the Encoding of Boundaries; Description of the Project WFcompressPal; The Project WFrestoreLin; Chapter 9: Image Segmentation and Connected Components; Segmentation by Quantizing the Colors; Connected Components; The Graph Traversal Algorithm and Its Code; The Pseudo-Code of the Breadth-First Algorithm; The Approach of Equivalence Classes
- The Pseudo-Code of the Root AlgorithmThe Project WFsegmentAndComp; Conclusion; Chapter 10: Straightening Photos of Paintings; The Principle of Straightening; Codes of Most Important Methods; Conclusion; Chapter 11: Polygonal Approximation of Region Boundaries and Edges; The Problem of Polygonal Approximation; Schlesinger's Measure of Similarity of Curves; Statement of the Approximation Problem; Algorithms for Polygonal Approximation; The Split-and-Merge Method; The Sector Method; The Improvement of the Sector Method; Replacing Polygons by Sequences of Arcs and Straight Lines
- Definitions and the Problem StatementThe Approximate Solution; The Project WFpolyArc; Methods Used in the Project WFpolyArc; Precision of the Calculation of the Radii; Conclusion; Chapter 12: Recognition and Measurement of Circular Objects; Mathematical Foundation of the Method; The Project WFcircleReco; The Form of the Project WFcircleReco; Chapter 13: Recognition of Bicycles in Traffic; Mathematical Foundation of Ellipse Recognition; The Project WFellipseBike; Another Method of Recognizing the Direction; Chapter 14: A Computer Model of Cell Differentiation; Conclusion; References; Index