DocumentCode :
2770727
Title :
A unified object-oriented toolkit for discrete contextual computer vision
Author :
Du, L. ; Downton, A.C. ; Lucas, S.M.
Author_Institution :
Dept. of Electron. Syst. Eng., Essex Univ., Colchester, UK
fYear :
1997
fDate :
35487
Firstpage :
42430
Lastpage :
42434
Abstract :
This paper describes a new general-purpose contextual architecture which provides a unified framework for efficiently combining all types and levels of context in discrete computer vision applications, by organising the multidimensional search space in best-first order along each dimension. It then implements an efficient `lazy evaluation´ algorithm, which searches from the most probable vertex outwards, and guarantees to find solutions in absolute best-first order. The architecture has been designed and built as a C++ class library, and utilised within a demonstrator which implements full contextual constraints for optical character recognition of hand-printed postal addresses. Preliminary evaluation of the demonstrator suggests the system has the potential to achieve genuinely remarkable performance compared with previous context systems: its memory requirements are an order of magnitude less than an equivalent trie-based dictionary; its search speed is at least an order of magnitude faster than the trie, and actually get faster as the dictionary size increases; and its error rate is virtually zero, even when an OCR system with appalling performance is simulated. Using this architecture it appears to be possible to build real-time solutions to large-scale contextual vision problems which have previously been beyond the bounds of computational feasibility
Keywords :
computer vision; C++ class library; OCR; computer vision; discrete contextual computer vision; general-purpose contextual architecture; hand-printed postal addresses; lazy evaluation; most probable vertex; multidimensional search space; optical character recognition; trie-based dictionary; unified object-oriented toolkit;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Pattern Recognition (Digest No. 1997/018), IEE Colloquium on
Conference_Location :
London
Type :
conf
DOI :
10.1049/ic:19970126
Filename :
598538
Link To Document :
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