NDLI: Predicate Logic Based Image Grammars for Complex Pattern Recognition

Content Provider	Springer Nature Link
Author	Shet, Vinay Singh, Maneesh Bahlmann, Claus Ramesh, Visvanathan Neumann, Jan Davis, Larry
Copyright Year	2010
Abstract	Predicate logic based reasoning approaches provide a means of formally specifying domain knowledge and manipulating symbolic information to explicitly reason about different concepts of interest. Extension of traditional binary predicate logics with the bilattice formalism permits the handling of uncertainty in reasoning, thereby facilitating their application to computer vision problems. In this paper, we propose using first order predicate logics, extended with a bilattice based uncertainty handling formalism, as a means of formally encoding pattern grammars, to parse a set of image features, and detect the presence of different patterns of interest. Detections from low level feature detectors are treated as logical facts and, in conjunction with logical rules, used to drive the reasoning. Positive and negative information from different sources, as well as uncertainties from detections, are integrated within the bilattice framework. We show that this approach can also generate proofs or justifications (in the form of parse trees) for each hypothesis it proposes thus permitting direct analysis of the final solution in linguistic form. Automated logical rule weight learning is an important aspect of the application of such systems in the computer vision domain. We propose a rule weight optimization method which casts the instantiated inference tree as a knowledge-based neural network, interprets rule uncertainties as link weights in the network, and applies a constrained, back-propagation algorithm to converge upon a set of rule weights that give optimal performance within the bilattice framework. Finally, we evaluate the proposed predicate logic based pattern grammar formulation via application to the problems of (a) detecting the presence of humans under partial occlusions and (b) detecting large complex man made structures as viewed in satellite imagery. We also evaluate the optimization approach on real as well as simulated data and show favorable results.
Starting Page	141
Ending Page	161
Page Count	21
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	93
Issue Number	2
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2010-09-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Stochastic image grammars Logical reasoning Human detection Object detection and classification Bilattice Back propagation Aerial image analysis Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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