Arthur Nádas
IEEE Transactions on Neural Networks
The storage requirements for component labeling and fea ture extraction operations are unknown a priori. Whenever large images are processed, many labels, and thus a large amount of storage, may be required, making hardware implementation difficult. The proposed labeling procedure eliminates memory overflow by enabling the reuse of memory locations in which features of nonactive labels had been stored. The storage requirement for the worst case conditions is analyzed and is shown to be realizable. The basic procedure can be implemented in two modes, an interrupted mode or a parallel mode. A hardware design is presented. Copyright © 1985 by The Institute of Electrical and Electronics Engineers, Inc.
Arthur Nádas
IEEE Transactions on Neural Networks
Freddy Lécué, Jeff Z. Pan
IJCAI 2013
Wang Zhang, Subhro Das, et al.
ICASSP 2025
Shai Fine, Yishay Mansour
Machine Learning