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Titlebook: Algorithms for Graphics and Image Processing; Theo Pavlidis Book 1982 Computer Science Press, Inc. 1982 algorithm.algorithms.image process

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期刊全稱Algorithms for Graphics and Image Processing
影響因子2023Theo Pavlidis
視頻videohttp://file.papertrans.cn/154/153226/153226.mp4
圖書封面Titlebook: Algorithms for Graphics and Image Processing;  Theo Pavlidis Book 1982 Computer Science Press, Inc. 1982 algorithm.algorithms.image process
影響因子The technological developments of the last ten years have made com- puter graphics and image processing by computer popular. Pictorial pat- tern recognition has also shown significant progress. Clearly, there exist overlapping interests among the three areas of research. Graphic displays are of concern to anyone involved in image processing or pic- torial pattern recognition and many problems in graphics require methodologies from image processing for their solutions. The data structures used in all three areas are similar. It seems that there is a common body of knowledge underlying all three areas, pictorial informa- tion processing by computer. The novelty of these fields makes it difficult to design a course or to a write a book covering their basic concepts. Some of the treatises on graphics focus on the hardware and methods of current interest while treatises on image processing often emphasize applications and classical signal processing. The fast evolution of technology causes such material to lose its relevance. For example, the development of optical fibers has reduced the importance of bandwidth compression.
Pindex Book 1982
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,Unternehmer nach H?fetradition,articular directions. (In graphics, as well as in some scene analysis problems, the term is commonly used to denote the mapping of a three-dimensional object to the plane with all depth information lost.) The . of gray scale images from their projections is one of the best known uses of computers fo
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,Das Comeback des Kalle-B?ckers,study of such images is the concept of ., a term that is not easy to define quantitatively. One faces this problem when sampling an analog bilevel image. The size of the cells of the sampling grid must be small enough so that the shapes of regions of a given color remain unaltered in reconstructing
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,Unternehmer nach H?fetradition,e has a local property if local modifications do not propagate. Clearly, the polynomials discussed in Section 10.2 do not have this feature and Bezier polynomials exhibit it only approximately. A change in the location or multiplicity of one of the guiding points requires the recalculation of the wh
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https://doi.org/10.1007/978-3-8349-9611-4meters, so that a curve defined by them can still be used for interpolation.) There are many applications where interpolation is not necessary, or even desirable, and one wants the curve to pass only near the data points. This is the approximation problem. If the curve fitting is done in an interact
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