Implementation of efficient algorithms for the computation of morphological texture features /
10000XL). Parallel processing implementation on two
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1998.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy |
| Summary: | 10000XL). Parallel processing implementation on two 11 based Dell 400 workstation, and SGI Power Challenge and classification. Among texture features, and compared. Those found efficient are then and Dell 400 workstation) is also performed; followed approach on an advanced DSP processor - TMS320C80. approaches also require a considerable amount of approaches for computing granulometry are discussed benchmarked on three machines (Sun SPARC 20, Pentium computation time, traditional granulometry computing computing the granulometry texture features for efficient ways to compute grey-scale granulometry. elements are used. However, for many images such as factors in the use of morphological granulometric features. These features are increasingly being found In many image analysis applications, objects of rographics. interest exhibit different texture than their interpretation. Hence, there is a need to find investigate, and implement efficient methods of least utilized partly due to the immense amount of linear sub-structures. This work is an attempt to made even greater when large images, such as mammograms are to be analyzed. In addition to high medical images, textures are rarely formed by hat memory. These factors are the major restraining morphological granulometry texture features are the multiprocessor machines (SGI Power Challenge IOOOOXL obtaining a granulometry. The computational problem is scale granulometry when flat linear structuring so when Some fast approaches are available for computing grey- structuring elements of any geometry are utilized in structuring elements of any geometry. First various surrounding areas. Texture features have been used as the initial step towards elective image segmentation time needed for their computation. This is especially to be elective in applications such as medical image |
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| Item Description: | "Major subject: Electrical Engineering". Vita. |
| Physical Description: | xiii, 65 leaves : illustrations ; 28 cm. Also available online. |
| Bibliography: | Includes bibliographical references: pages 63-64. |