By Prof. Dr. Thomas Bräunl, Dipl.-Inform. Stefan Feyrer, Dipl.-Inform. Wolfgang Rapf, Dipl.-Inform. Michael Reinhardt (auth.)
ISBN-10: 3642086799
ISBN-13: 9783642086793
ISBN-10: 3662043270
ISBN-13: 9783662043271
This ebook built out of a chain of guides within the region of photo processing with vastly parallel algorithms. the subject of photograph processing is a very promising region for using synchronous vastly parallel or data-parallel compu ter structures which paintings in response to the SIMD precept (single guide, a number of data). whereas the period of huge SIMD super-computers has handed, SIMD structures have come again as devoted imaginative and prescient subsystems and should quickly be came across even in embedded platforms. compared to traditional sequential implementations of easy photograph opera tions, this e-book illustrates the intrinsic parallelism that is regularly found in photo processing. via applying parallel algorithms it really is even attainable to demonstrate oper ations in a less complicated and more straightforward to appreciate manner than for the sequential case. The presentation approach selected for this ebook assumes that brief, terse excerpts of application code will considerably improve the knowledge of the cloth, e.g. of photo operations, whereas longer listings usually tend to distract from the subject. accordingly, every one bankruptcy won't in basic terms outline and clarify the important snapshot processing algorithms with the aid of examples, yet also will provide an excerpt of a hugely par allel application. For photograph processing which means not less than nearly there might be one processor on hand for every pixel. The mapping onto a smaller variety of exist by way of compiler, and as of ing actual processors is finished transparently the such isn't curiosity here.
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Example text
This means savings in data exchanges as well as computational operations. 1 shows the two basic operators with an example for structure-element S1 (3x3 TRUE). Whereas erosion deletes almost the entire object in the foreground, and only leaves those pixels which had been completely enclosed by object pixels (8neighbourhood), dilation enlarges the object by placing new pixels around each of the object pixels. ,- r ,. 1 shows the parallel implementation of erosion and dilation. The implementation chosen here uses the simple 3x3-structure-element S1.
To determine deletability the neighbourhood types 1, 4, 5, 10, 11, 12, 20, 21, 24, 33 and 34 are examined. After this the neigh- 5 Skeletonizing 38 • • •. 3: Neighbourhood types bourhood types 4, 5, 10, 11, 12, 16, 20, 21, 24, 28, 33, 34, 35, 38, 42,46 and 50 are applied to facilitate the actual peeling process. To ensure that the skeleton lines are in the centre of the image objects, the image is split into four areas which are processed one after the other. 3) this constitutes an algorithm that acts on a 3x3 neighbourhood, has four iterations, the same skeletonizing operator, and four different sub-sections.
To achieve simplified and faster processing as well as reduced memory requirements, it is often useful to convert the original image data to a more compact representation. This conversion should remove as much of the redundant information as possible, but must preserve the basic structure of the digitized image. g. text or line drawings, a number of methods exist that will extract so-called skeletons from the original images. These are called skeletonizing operators or thinning-operators. 1 Introduction Methods of the above kind are applied to binary images.
Parallel Image Processing by Prof. Dr. Thomas Bräunl, Dipl.-Inform. Stefan Feyrer, Dipl.-Inform. Wolfgang Rapf, Dipl.-Inform. Michael Reinhardt (auth.)
by Christopher
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