Hypercube Algorithms for Image Transformations

Efficient hypercube algorithms are developed for the following image transformations: shrinking, expanding, translation, rotation, and scaling. A 2k −step shrinking and expanding of a gray scale N×N image can be done in O (k) time on an N2 processor MIMD hypercube and in O (logN) time on an SIMD hypercube. Translation, rotation, and scaling of an N×N image take O (logN) time on an N2 processor hypercube. Keywords and Phrases Hypercube multicomputers, SIMD, MIMD, shrinking, expanding, rotation, translation, scaling. __________________ + This research was supported in part by the National Science Foundation under grants DCR84-20935 and MIP 86-17374 1 

• Type: Conference
• Year: 1989
• Where: ICPP
• Authors: Sanjay Ranka, Sartaj Sahni
• Keywords: Distributed And Parallel Computing, Efficient Hypercube Algorithms, Hypercube, ICPP 1989, Processor Mimd Hypercube