SYMBOL RECOGNITION IN A CAD ENVIRONMENT USING A NEURAL NETWORK
| العنوان: | SYMBOL RECOGNITION IN A CAD ENVIRONMENT USING A NEURAL NETWORK |
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| المؤلفون: | Der-Shung Yang, James H. Garrett, Doris S. Shaw, Julie L. Webster, Larry A. Rendell |
| المصدر: | International Journal on Artificial Intelligence Tools. :157-185 |
| بيانات النشر: | World Scientific Pub Co Pte Lt, 1994. |
| سنة النشر: | 1994 |
| مصطلحات موضوعية: | Structure (mathematical logic), Similarity (geometry), Artificial neural network, Computer science, Orientation (computer vision), CAD, computer.file_format, computer.software_genre, Symbol (chemistry), Domain (software engineering), Artificial Intelligence, Bitmap, Data mining, computer |
| الوصف: | A new neural network called AUGURS is designed to assist a user of a Computer-Aided Design system in utilizing standard graphic symbols. With AUGURS, the CAD user can avoid searching for standard symbols in a large library and rely on AUGURS to automatically retrieve those symbols resembling the user’s drawing. More specifically, AUGURS inputs a bitmap image normalized with respect to location, size, and orientation, and outputs a list of standard symbols ranked by its assessment of the similarity between the symbol and the input image. Only the top ranked symbols are presented to the user for selection. AUGURS encodes geometric knowledge into its network structure and carefully balances its discriminant power and noise tolerance. The encoded knowledge enables AUGURS to learn reasonably well despite the limited number of training examples, the most serious challenge for the CAD domain. We have compared AUGURS with the Zipcode Net, a traditional layered feed-forward network with an unconstrained structure, and a network that inputs either Zernike or pseudo-Zernike moments. The experimental results conclude that AUGURS can achieve the best recognition performance among all networks being compared with reasonable recognition and learning efficiency. |
| تدمد: | 1793-6349 0218-2130 |
| DOI: | 10.1142/s0218213094000091 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::91a311200fcc23eb52154c76632fbd81 https://doi.org/10.1142/s0218213094000091 |
| رقم الانضمام: | edsair.doi...........91a311200fcc23eb52154c76632fbd81 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17936349 02182130 |
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| DOI: | 10.1142/s0218213094000091 |