Being able to predict events and occurrences which may arise from a current situation is a desirable capability of an intelligent agent. In this paper, we show that a high-level scene interpretation system, implemented as part of a comprehensive robotic system in the RACE project, can also be used for prediction. This way, the robot can foresee possible developments of the environment and the effect they may have on its activities. As a guiding example, we consider a robot acting as a waiter in a restaurant and the task of predicting possible occurrences and courses of action, e.g. when serving a coffee to a guest. Our approach requires that the robot possesses conceptual knowledge about occurrences in the restaurant and its own activities, represented in the standardized ontology language OWL and augmented by constraints using SWRL. Conceptual knowledge may be acquired by conceptualizing experiences collected in the robot’s memory. Predictions are generated by a model-construction process which seeks to explain evidence as parts of such conceptual knowledge, this way generating possible future developments. The experimental results show, among others, the prediction of possible obstacle situations and their effect on the robot actions and estimated execution times. 1

In recent years, several methods have been proposed for content-based retrieval from manuscripts, mostly based on character or word similarity. In this paper, we present a new segmentation-free method, called Harris Corner Matching (HCM), which accepts an arbitrary writing pattern as a model and allows to retrieve similar patterns from a possibly large database. Retrieval is performed in two steps. In the first step, candidate targets are determined by comparing configurations of interest points in query and data. In fact, interest points can be used as precomputed indices. In the second step, deviations between the interest point configurations of query and candidate target are used to warp the target, this way adapting it to the query. A final evaluation is obtained by template matching after binarization of query and target. The method has been evaluated for retrieval from historical Chinese and Sanskrit manuscripts and has shown good results.

Modern digital signal processing applications have an increasing demand for computational power while needing to preserve low power dissipation and high flexibility. For many applications, the growth of algorithmic complexity is already faster than the growth of computational power provided by discrete general purpose processors. A typical approach to address this problem is the combination of a processor core with dedicated accelerators. Since changes in standards or algorithms can change the demands on the accelerators, an attractive alternative to highly customised VLSI- macros is the use of reconfigurable embedded FPGAs (eFPGAs). First commercial products combining a general purpose processor core and an embedded FPGA recently emerged (e.g. Stretch S6000 Menta eFPGA- augmented CPUs). For many digital signal processing applications, a significantly improved efficiency in terms of power dissipation, throughput and chip area can be achieved by tailoring both the processor core and the reconfigurable accelerator to the given application domain. In this work, a methodology to design highly customisable eFPGA-architectures starting from a high level description is presented. The design framework elaborated during this work enables a physically optimised VLSI-design of the specified eFPGA and aims to support simulation of the according eFPGA-macros both on a functional and netlist-level by providing an elementary configuration tool based on the same high level description as the eFPGA-architecture.

Ontological concept descriptions of scene objects and aggregates play an essential role in model-based scene interpretation. An aggregate specifies a set of objects with certain properties and relations which together constitute a meaningful scene entity. In this paper we show how ontological concept descriptions for spatially related objects and aggregates can be learnt from positive and negative examples. Our approach features a rich representation language encompassing quantitative and qualitative attributes and relations. Using examples from the buildings domain, we show that learnt aggregate concepts for window arrays, balconies and other structures can be successfully employed in the conceptual knowledge base of a scene interpretation system. Furthermore we argue that our approach can be extended to cover ontological concepts of any kind, with very few restrictions.

Embedding FPGAs (eFPGAs) in modern SoCs provides a high amount of flexibility while high-throughput digital signal processing algorithms can be realised efficiently. An analysis of eFPGA architectures and corresponding structural elements is presented to determine the optimisation potential for eFPGAs tailored to an arithmetic oriented application domain. The applied design flow incorporating an automated layout generation approach and the utilised simulation environment is discussed. An eFPGA macro designed and realised for arithmetic oriented applications is quantitatively compared to an actual commercial FPGA in terms of area, power consumption and delay time. It can be shown that this optimised eFPGA macro outperforms a state of the art commercial device for a couple of arithmetic operators which are commonly applied in arithmetic datapaths

In this contribution, we present a formal, logic-based approach to image interpretation by combining methods from two different research areas, namely computer vision and knowledge representation. After describing two well known approaches we present a concise definition of the required solution of an image understanding problem. We then propose an object-centered, KL-ONE-like description logic tailored to the representation needs in image understanding and a calculus for computing an interpretation of a given image according to our definition.

The problem of converting paper drawings into computer-based representations suitable for further use in CAD and CIM applications is addressed. While previous approaches have been able to solve this problem for certain application domains, the kernel system presented is adaptable to a wide variety of applications including engineering drawings and electronic circuit diagrams. The key elements of the approach presented are a knowledge-based interpretation process and a declarative specification language for defining meaningful graphical objects to be recognized in a drawing. The potential efficiency problems of a strictly declarative approach are overcome by automatically compiling graphic specifications into efficient procedures. The adaptability and efficiency of the kernel system are demonstrated using examples from several domains.>