Please use this identifier to cite or link to this item:
|Title:||Image reconstruction by a Hopfield neural network||Authors:||Srinivasan, V.
|Issue Date:||Jun-1993||Citation:||Srinivasan, V.,Han, Y.,Ong, S. (1993-06). Image reconstruction by a Hopfield neural network. Image and Vision Computing 11 (5) : 278-282. ScholarBank@NUS Repository.||Abstract:||The reconstruction of cross-sectional images from projections involves the solution of a large system of simultaneous equations in which the unknowns are attentuation coefficients associated with the cells consti tuting the image. As an alternative to iterative methodssuch as the algebraic reconstruction technique (ART), a modified linear form of the Hopfield neural network with a summation layer to significantly decrease the number of interconnections is proposed. Higher speed and improved SNR, compared to ART, have been obtained on the Shepp and Logan 'head phantom' divided into 100 × 100 cells. © 1993.||Source Title:||Image and Vision Computing||URI:||http://scholarbank.nus.edu.sg/handle/10635/62302||ISSN:||02628856|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 18, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.