Inverse problems in vision and 3D tomography /
The concept of an inverse problem is a familiar one to most scientists and engineers, particularly in the field of signal and image processing, imaging systems (medical, geophysical, industrial non-destructive testing, etc.) and computer vision. In imaging systems, the aim is not just to estimate un...
| Uniform Title: | Problemes inverses en imagerie et en vision. |
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| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
London : Hoboken, NJ :
ISTE ; Wiley,
2010.
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| Series: | Digital signal and image processing series.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- Cover; Title Page; Copyright Page; Table of Contents; Preface; Chapter 1. Introduction to Inverse Problems in Imaging and Vision; 1.1. Inverse problems; 1.1.1. 1D signal case; 1.1.2. Convolution model for image restoration; 1.1.3. General linear model; 1.2. Specific vision problems; 1.2.1. Noise removal; 1.2.2. Segmentation; 1.2.3. Contour detection; 1.2.4. Restoration or reconstruction in vision; 1.3. Models for time-dependent quantities; 1.4. Inverse problems with multiple inputs and multiple outputs (MIMO); 1.4.1. MIMO deconvolution; 1.4.2. Super-resolution; 1.4.3. Source separation
- 1.5. Non-linear inverse problems1.5.1. Microwave imaging; 1.6. 3D reconstructions; 1.6.1. Reconstruction of the surface of a 3D object from its radiographs; 1.7. Inverse problems with multimodal observations; 1.7.1. Fusion of X-ray radiography and ultrasonic echography data; 1.8. Classification of inversion methods: analytical or algebraic; 1.8.1. Analytical inversion methods; 1.8.2. Analytical inversion methods in a dual space; 1.8.3. Discretization into pixels or voxels, and algebraic inversion; 1.9. Standard deterministic methods; 1.9.1. Matched filter or back-projection solution
- 1.9.2. Inverse solution in the classical sense1.9.3. Minimum-norm solution; 1.9.4. Least-squares solution; 1.9.5. The regularized solution; 1.9.6. Finding an inverse operator; 1.10. Probabilistic methods; 1.10.1. Bayesian estimation approach; 1.11. Problems specific to vision; 1.12. Introduction to the various chapters of the book; 1.12.1. Noise removal and contour detection; 1.12.2. Blind image deconvolution; 1.12.3. Triplet Markov chains and image segmentation; 1.12.4. Detection and recognition of a collection of objects in a scene; 1.12.5. Apparent motion estimation and visual tracking
- 1.12.6. Super-resolution1.12.7. Tomographic surface reconstruction; 1.12.8. Gauss-Markov-Potts prior for Bayesian inversion in microwave imaging; 1.12.9. 3D reconstruction from shadows; 1.12.10. Image separation; 1.12.11. Stereo reconstruction from satellite or aerial photography; 1.12.12. Fusion and multimodality; 1.13. Bibliography; Chapter 2. Noise Removal and Contour Detection; 2.1. Introduction; 2.1.1. Boolean line process models; 2.1.2. Half-quadratic regularization; 2.1.3. Comments; 2.1.4. Constraints on the contour variables; 2.1.5. Regularization of intensity and region segmentation
- 2.2. Statistical segmentation of noisy images2.2.1. Noise models; 2.2.2. Multiplicative noise in sonar and radar imagery; 2.2.3. Independent Gaussian noise; 2.2.4. Correlated Gaussian noise; 2.2.5. Generalized Gaussian noise; 2.3. Multi-band multi-scale Markovian regularization; 2.3.1. Bayesian inference; 2.3.2. Cost function; 2.3.3. Hierarchical Markovian models; 2.3.4. Markovian quad-tree; 2.3.5. Missing or erroneous data; 2.4. Bibliography; Chapter 3. Blind Image Deconvolution; 3.1. Introduction; 3.2. The blind deconvolution problem; 3.2.1. Ill-posed problem (multiple solutions)