Radon and projection transform-based computer vision : algorithms, a pipeline architecture, and industrial applications
著者
書誌事項
Radon and projection transform-based computer vision : algorithms, a pipeline architecture, and industrial applications
（Springer series in information sciences, 16）
Springer-Verlag, c1988
- : U.S.
- : Germany
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This book deals with novel machine vision architecture ideas that make real-time projection-based algorithms a reality. The design is founded on raster-mode processing, which is exploited in a powerful and flexible pipeline. We concern ourselves with several image analysis algorithms for computing: projections of gray-level images along linear patterns (i. e. , the Radon transform) and other curved contours; convex hull approximations; the Hough transform for line and curve detection; diameters; moments and principal components, etc. Addition- ally, we deal with an extensive list of key image processing tasks, which involve generating: discrete approximations of the inverse Radon transform operator; computer tomography reconstructions; two-dimensional convolutions; rotations and translations; multi-color digital masks; the discrete Fourier transform in polar coordinates; autocorrelations, etc. Both the image analysis and image processing algorithms are supported by a similar architecture. We will also of some of the above algorithms to the solution of demonstrate the applicability various industrial visual inspection problems.
The algorithms and architectural ideas surveyed here unleash the power of the Radon and other non-linear transformations for machine vision applications. We provide fast methods to transform images into projection space representa- tions and to backtrace projection-space information into the image domain. The novelty of this approach is that the above algorithms are suitable for implementa- tion in a pipeline architecture. Specifically, random access memory and other dedicated hardware components which are necessary for implementation of clas- sical techniques are not needed for our algorithms.
目次
1. Introduction.- 1.1 Machine Vision Architectures.- 1.2 The Radon Transform and the PPPE Architecture.- 2. Model and Computation of Digital Projections.- 2.1 Representation of Digital Lines.- 2.2 Generation of Projection Data.- 2.3 Noise Considerations.- 3. Architectures.- 3.1 The Contour Image Generator.- 3.2 The Projection Data Collector.- 3.3 Additional Hardware.- 3.4 Putting It All Together: P3E.- 3.5 Implementation in Commercially Available Pipelines.- 4. Projections Along General Contours.- 5. P3E-Based Image Analysis Algorithms and Techniques.- 5.1 Computing Convex Hulls, Diameters, Enclosing Boxes, Principal Components, and Related Features.- 5.2 Computing Hough Transforms for Line and Curve Detection.- 5.3 Generating Polygonal Masks.- 5.4 Generating Multi-Colored Masks.- 5.5 Non-Linear Masks.- 6. P3E-Based Image Processing Algorithms and Techniques.- 6.1 Non-iterative Reconstruction.- 6.1.1 Convolution Backprojection.- 6.1.2 Filtered Backprojection.- 6.2 Iterative Reconstruction.- 6.2.1 The Kacmarz Method.- 6.3 Two-Dimensional Convolution.- 6.4 Rotation and Translation.- 6.5 Computerized Tomography Reconstruction.- 6.6 Autocorrelation.- 6.7 Polar Fourier Transform and Object Classification.- 7. Radon Transform Theory for Random Fields and Optimum Image Reconstruction from Noisy Projections.- 7.1 Radon Transform Theory of Random Fields.- 7.2 Optimum Reconstruction from Noisy Projections.- 8. Machine Vision Techniques for Visual Inspection.- 9. Conclusion.
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