3191837
Active 3-D Object Recognition Using Appearance-Based Aspect Graphs
We present a new active active recognition scheme (using an uncalibrated camera) based on a new idea, appearancebased aspect graphs. The scheme is robust to background clutter, and affine transformations of the object. We use a probabilistic reasoning framework which helps in probability calculations and planning the next view (when a view of the object does not contain sufficient features to recognise it unambiguously), in conjunction with a new hierarchical knowledge representation scheme. Preliminary experiments with the system show encouraging results.
6539802
Robot localization using uncalibrated camera invariants
We describe a set of image measurements which are invariant to the camera internals but are location variant. We show that using these measurements it is possible to calculate the self-localization of a robot using known landmarks and uncalibrated cameras. We also show that it is possible to compute, using uncalibrated cameras, the Euclidean structure of 3-D world points using multiple views from known positions. We are free to alter the internal parameters of the camera during these operations. Our initial experiments demonstrate the applicability of the method.
30169882
Aspect graph construction with noisy feature detectors
Many three-dimensional (3D) object recognition strategies use aspect graphs to represent objects in the model base. A crucial factor in the success of these object recognition strategies is the accurate construction of the aspect graph, its ease of creation, and the extent to which it can represent all views of the object for a given setup. Factors such as noise and nonadaptive thresholds may introduce errors in the feature detection process. This paper presents a characterization of errors in aspect graphs, as well as an algorithm for estimating aspect graphs, given noisy sensor data. We present extensive results of our strategies applied on a reasonably complex experimental set, and demonstrate applications to a robust 3D object recognition problem.
17398921
Design issues for a high-performance distributed shared memory on symmetrical multiprocessor clusters
Clusters of Symmetrical Multiprocessors (SMPs) have recently become the norm for high-performance economical computing solutions. Multiple nodes in a cluster can be used for parallel programming using a message passing library. An alternate approach is to use a software Distributed Shared Memory (DSM) to provide a view of shared memory to the application programmer. This paper describes Strings, a high performance distributed shared memory system designed for such SMP clusters. The distinguishing feature of this system is the use of a fully multi-threaded runtime system, using kernel level threads. Strings allows multiple application threads to be run on each node in a cluster. Since most modern UNIX systems can multiplex these threads on kernel level light weight processes, applications written using Strings can exploit multiple processors on a SMP machine. This paper describes some of the architectural details of the system and illustrates the performance improvements with benchmark programs from the SPLASH-2 suite, some computational kernels as well as a full fledged application. It is found that using multiple processes on SMP nodes provides good speedups only for a few of the programs. Multiple application threads can improve the performance in some cases, but other programs show a slowdown. If kernel threads are used additionally, the overall performance improves significantly in all programs tested. Other design decisions also have a beneficial impact, though to a lesser degree.
26846276
Hierarchical representation of digitized curves through dominant point detection
Hierarchical data structures play a key role in representation of spatial data. This paper proposes an algorithm for constructing hierarchical representations of digitized curves based on the dominant points of the curve. The comparison of the proposed scheme with the other hierarchical representation schemes viz. Arc Tree and Strip Tree reveals that the proposed algorithm is generally better than the Arc Tree and compares well with the Strip Tree scheme.
9104858
Moment-Linear Stochastic Systems
We introduce a class of quasi-linear models for stochastic dynamics, called moment-linear stochastic systems (MLSS). We formulate MLSS and analyze their dynamics, as well as discussing common stochastic models that can be represented as MLSS. Further studies, including development of optimal estimators and controllers, are summarized. We discuss the reformulation of a common stochastic hybrid system—the Markovian jump- linear system (MJLS)—as an MLSS, and show that the MLSS formulation can be used to develop some new analyses for MJLS. Finally, we briefly discuss the use of MLSS in modeling certain stochastic network dynamics. Our studies suggest that MLSS hold promise in providing a framework for modeling interesting stochastic dynamics in a tractable manner.
12929901
Completeness of Neighbourhood Logic
This paper presents a completeness result for a first-order interval temporal logic, called Neighbourhood Logic (NL) which has two neighbourhood modalities. NL can support the specification of liveness and fairness properties of computing systems as well as formalisation of many concepts of real analysis. These two modalities are also adequate in the sense that they can derive other important unary and binary modalities of interval temporal logic. We prove the completeness result for NL by giving a Kripke model semantics and then mapping the Kripke models to the interval models for NL.
37032385
Completeness of neighbourhood logic