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.
1879700
Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker
We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set.
14932066
On line predictive appearance-based tracking
We present a novel predictive statistical framework to improve the performance of an eigentracker. In addition, we use fast and efficient eigenspace updates to learn new views of the object being tracked on the fly. We also incorporate a new importance sampling mechanism which increases the robustness of the eigentracker and enables it to track nonconvex objects better. Our eigentracker is flexible-it is possible to use it symbolically with other trackers. We show its successful application in hand gesture analysis; and face and person tracking.
18611387
Robust Fingerprint Classification Using An Eigen Block Directional Approach
This paper describes a method of fingerprint classification using Eigen Block Directional Fingerprints. The method we propose dispenses off with the preprocessing stages such as segmentation, binarisation, thinning etc. We determine the block directional representation of a fingerprint and the location of the core points in a set of template fingerprints which belong to the same class. In the next step, we determine the most prominent eigen vectors of each template, which we term as the Eigen Block Directional Fingerprints. To determine the classification of a fingerprint, we extract the Block Directional Fingerprint of the query image and determine the alignment parameters between the template and the query images using eigen tracker which minimises the robust error. We declare the class of the query image as the class of the Eigen Block Directional Fingerprint which result in the least robust error norm.
9598402
Time-space tradeoffs in the counting hierarchy
Extends the lower-bound techniques of L. Fortnow (2000) to the unbounded-error probabilistic model. A key step in the argument is a generalization of V.A. Nepomnjas/spl caron/c/spl caron/ii/spl breve/'s (1970) theorem from the Boolean setting to the arithmetic setting. This generalization is made possible due to the recent discovery of logspace-uniform TC/sup 0/ circuits for iterated multiplication (A. Chiu et al., 2000). As an example of the sort of lower bounds that we obtain, we show that MAJ-MAJSAT is not contained in PrTiSp(n/sup 1+o(1)/, n/sup /spl epsiv//) for any /spl epsiv/<1. We also extend one of Fortnow's lower bounds, from showing that S~A~T~ does not have uniform NC/sup 1/ circuits of size n/sup 1+o(1)/, to a similar result for SAC/sup 1/ circuits.
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.
2324494
Cost-Performance Evaluation of SMP Clusters
Clusters of Personal Computers have been proposed as potential replacements for expensive compute servers. One limitation in the overall performance is the interconnection network. A possible solution is to use multiple processors on each node of the PC cluster. Parallel programs can then use the fast shared memory to exchange data within a node, and access the interconnection network to communicate across multiple nodes. The performance of such a system is likely to be influenced by the runtime support system, whether one uses a standard message passing library or a distributed shared memory system to simulate a large shared memory machine. This paper presents a cost-performance evaluation of a cluster of eight dual processor PCs and a ten processor SMP workstation using two benchmark suites. We evaluate the effect of using gigabit ethernet against fast ethernet, single processor PCs versus SMP nodes, and compare the overall performance of the PC cluster to the SMP workstation. It is found that the SMP machine provides the best performance in only 50 % of the programs examined. The PC cluster with a Gigabit network is better in most of the other cases, but the improvement over FastEthernet is marginal.
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.
9930427
An algorithm for converting floating-point computations to fixed-point in MATLAB based FPGA design
Most practical FPGA designs of digital signal processing applications are limited to fixed-point arithmetic owing to the cost and complexiry of floating-point hardware. While mapping DSP applications onto FPGAs, a DSP algorithm designer, who often develops his applications in MATLAB, must determine the dynamic range and desired precision of input, intermediate and output signals in a design implementation to ensure that the algorithm fidelity criteria are met. The first step in a flow to map MATLAB applications into hardware is the conversion of the floating-point MATLAB algorithm into a fixed-point version. This paper describes an approach to automate this conversion, for mapping to FPGAs by profiling the expected inputs to estimate errors. Our algorithm attempts to minimize the hardware resources while constraining the quantization error within a specified limit