Titlebook: Radon and Projection Transform-Based Computer Vision; Algorithms, A Pipeli Jorge L. C. Sanz,Eric B. Hinkle,Anil K. Jain Textbook 1988 Sprin

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P3E-Based Image Processing Algorithms and Techniques,ng a digital approximation not only to the Radon transform, but also to the . Radon transform. Thus, we have focussed on image processing applications in which Radon data must be backprojected to recover the image. These applications are presented in the following sections, and they include: (1) dig

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Projections Along General Contours,e masks (see Sects. 5.4 and 5.5). Another example is the operation . , where . is a constant, which yields hyperbolic ramps.. In this case, it is necessary that the CIG be capable of taking integer products between the image coordinates.

Neolithic

P3E-Based Image Analysis Algorithms and Techniques,for detecting the maximum-likelihood position of linear and non-linear edges, and generating multi-color polygonal masks for model-based applications. The purpose of this chapter is to explain these algorithms in detail, and present experimental results illustrating their performance.

逃避现实

Radon Transform Theory for Random Fields and Optimum Image Reconstruction from Noisy Projections,m field and its two-dimensional power spectrum density. This result yields the optimum mean square reconstruction filter from noisy projections and is useful in other problems such as multi-dimensional spectral estimation from one-dimensional projections, noise analysis in computed tomography, etc.

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Conclusion,owerful pipeline architecture (P.E), which supports a wide variety of projection-based image analysis and image processing tasks. We have surveyed and presented several image analysis and image processing algorithms that are easily mapped onto P.E. We have shown favorable results for a variety of reconstruction techniques and applications.

Aviary

0720-678X d 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;

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