Abstract
In this paper, an extended work reported in [Shet, et al , 2007] to detect complex objects in aerial images was discussed. Such objects, e.g. surface to air missile launcher sites, are highly variable in appearance and can only be characterized by their functional design and surrounding context, such as physical arrangement of access structures. Constraints in acquiring sufficient annotated data for learning make it challenging for purely data driven approaches to adequately generalize. In this work, structure arising from functional requirements and surrounding context has been encoded using predicate logic based grammars. Observation and model uncertainties have been integrated within the bi lattice framework. Also in this paper a proposed method to automatically optimize weights associated with logical rules is presented. Automated logical rule weight learning is an important aspect of the application of such systems in the computer vision domain. The proposed approach casts the instantiated inference tree as a knowledge based neural net, interprets rule uncertainties as link weights in the network, and applies a constrained, back propagation (BP) algorithm to converge upon a set of weights for optimal performance. The BP algorithm has been accordingly modified to compute local gradients over the bi lattice specific inference operation and respect constraints specific to vision applications. Both extension have been evaluated over real and simulated data with favorable results.