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1 March 1992 Versatile architecture for image recognition applications
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Architectures for the development of image recognition algorithms must support the implementation of systematic procedures for solving image recognition problems. All too often, designers develop image recognition architectures in an ad hoc fashion which lacks the structure to meet long term needs. Vendors typically supply customers with standard image processing libraries and display tools. Combining these tools and formulating development strategies have remained stumbling blocks in the design of complete image recognition algorithm development environments. In this paper, an architecture is presented which provides a well defined framework, and at the same time is sufficiently flexible to accommodate images of multiple sensor and data types. The primary components of the architecture are: ground-truthing, preprocessing (which includes image processing and segmentation), feature extraction, classification, and performance analysis. Powerful and well defined data structures are exploited for each of the primary components. Groups of programs called tasks manipulate one or more of these data structures, each task belonging to one of the primary components. Multiple tasks can be executed in an unsupervised mode over an entire database of images. Results are then subjected to performance analysis and feedback. A description of the primary components and how they are integrated to facilitate the rapid prototyping and development of image recognition algorithms is presented.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Anthony Sacramone, Joseph Scola, and Dov J. Shazeer "Versatile architecture for image recognition applications", Proc. SPIE 1615, Machine Vision Architectures, Integration, and Applications, (1 March 1992);

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