Radon and Projection Transform-Based Computer Vision: Algorithms, A Pipeline Architecture, and Industrial ApplicationsThis 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. |
Contents
| 1 | |
Projections Along General Contours | 37 |
P³EBased Image Processing Algorithms and Techniques | 71 |
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Common terms and phrases
2-D convolution applications approach array autocorrelation backprojection technique backtracing binary image boundary brightfield Chap chip commercially available computer vision contour image convex hull approximation convex polygons convolution backprojection coordinate-reference image corresponding darkfield detection digital approximation digital image digital objects digital projection data discrete disk-head efficient example filtered backprojection filtered projections Fourier transform function given gradient image gray levels hardware histogramming horizontal profile Hough transform image analysis image pixel image reconstruction inverse Radon transform look-up table machine vision multiple noise non-iterative non-linear obtained orientation original image P³E parallel parameters patterns performed pipeline architecture pixel pixel coordinates pixel i,j pixel-by-pixel Po(t polygonal masks power spectrum problem processor pass projection space projection-based purpose image processing Radon space Radon transform random fields raster real-time regions registers representation require Sect segmentation semi-plane shown in Fig shows SIMD slice stage stationary random fields table look-up operation tesselation theorem thresholding two-dimensional