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.
5422021
Fast and Robust Projective Matching for Fingerprints Using Geometric Hashing
Fingerprint matching is the most important module in automatic person recognition using fingerprints. We model the nonlinear distortions and noise obtained during the fingerprint capture process as transformations in the projective domain. Matching of the fingerprints involves computing the homography matrix for the projective transformation, mapping of the minutia points by this homography and finally computing the points that match each other within a pre-determined threshold. We perform a fast match using a Geometric Hashing-based algorithm which matches the points in polynomial time. Preliminary experiments with a sample representative database show promising results.
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.
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.
14927396
Stabilisation of Active Contours Using Tangential Evolution: An Application to Tracking
Active contours are very widely used in computer vision problems. Their usage has a typical problem, that of bunching together of curve points. This becomes apparent especially when we use active contours for tracking, leading to instability in curve evolution. In this paper, we propose an additional tangential term to stabilise the evolution while at the same time ensure that the curve shape is not changed. The proposed method is simple and the computational overhead is minimal, while the results are good
1283699
ConvexSmooth: a simultaneous convex fitting and smoothing algorithm for convex optimization problems
Convex optimization problems are very popular in the VLSI design society due to their guaranteed convergence to a global optimal point. Table data is often fitted into analytical forms like posynomials to make them convex. However, fitting the look-up tables into posynomial forms with minimum error itself may not be a convex optimization problem and hence excessive fitting errors may be introduced. In recent literature numerically convex tables have been proposed. These tables are created optimally by minimizing the perturbation of data to make them numerically convex. But since these tables are numerical, it is extremely important to make the table data smooth, and yet preserve its convexity. Smoothness will ensure that the convex optimizer behaves in a predictable way and converges quickly to the global optimal point. In this paper, we propose to simultaneously create optimal numerically convex look-up tables and guarantee smoothness in the data. We show that numerically "convexifying" and "smoothing" the table data with minimum perturbation can be formulated as a convex semidefinite optimization problem and hence optimality can be reached in polynomial time. We present our convexifying and smoothing results on industrial cell libraries. ConvexSmooth shows 14times reduction in fitting error over a well-developed posynomial fitting algorithm
11274155
Minimum-Energy Broadcast Using Practical Directional Antennas in All-Wireless Networks
Energy-efficient broadcast communication is an important problem in wireless ad hoc networks. Previously, minimum-energy broadcast that exploits the broadcast nature of radio transmission has been studied and shown to be NP-complete for omnidirectional antennas. In this paper, we investigate the minimum-energy broadcast problem under a wide spectrum of directional antenna models, including sectored antennas with fixed sectors and beamwidth and antenna array-based smart antennas with varying degrees of beam orientation and beamwidth. We first propose the RF design and implementation of each model, which suggests the practical parameters of antennas under that model. We then show that the minimum-energy broadcast problem under each of the antenna models is NP-complete. Lastly, we present a heuristic algorithm based on BIP for the above problem under each directional antenna model and experimentally compare the energy efficiency of using different directional antennas using these heuristics. Our results show that using antennas with adjustable orientation and variable beamwidth gives the best results. For such antennas, the scanning angle is the dominant factor in improving the quality of the broadcast trees. The number of antennas per node is not that critical to obtaining a better broadcast tree as long as it is large enough to cover the entire 360◦ around a node so as to prevent network partitioning.
13205673
Automatic Generation of Domain Models for Call-Centers from Noisy Transcriptions
Call centers handle customer queries from various domains such as computer sales and support, mobile phones, car rental, etc. Each such domain generally has a domain model which is essential to handle customer complaints. These models contain common problem categories, typical customer issues and their solutions, greeting styles. Currently these models are manually created over time. Towards this, we propose an unsupervised technique to generate domain models automatically from call transcriptions. We use a state of the art Automatic Speech Recognition system to transcribe the calls between agents and customers, which still results in high word error rates (40%) and show that even from these noisy transcriptions of calls we can automatically build a domain model. The domain model is comprised of primarily a topic taxonomy where every node is characterized by topic(s), typical Questions-Answers (Q&As), typical actions and call statistics. We show how such a domain model can be used for topic identification of unseen calls. We also propose applications for aiding agents while handling calls and for agent monitoring based on the domain model.
6457662
ConvexFit: an optimal minimum-error convex fitting and smoothing algorithm with application to gate-sizing
Convex optimization has gained popularity due to its capability to reach global optimum in a reasonable amount of time. Convexity is often ensured by fitting the table data into analytically convex forms such as posynomials. However, fitting the look-up tables into the posynomial forms with minimum error itself may not be a convex optimization problem and hence excessive fitting errors may be introduced. In this paper, we propose to directly adjust the look-up table values into a numerically convex look-up table without explicit analytical form. We show that numerically "convexifying" the table data with minimum perturbation can be formulated as a convex semidefinite optimization problem and hence optimality can be reached in polynomial time. Without an explicit form limitation, we find that the fitting error is significantly reduced while the convexity is still ensured. As a result, convex optimization algorithms can still be applied. Furthermore, we also develop a "smoothing" algorithm to make the table data smooth and convex to facilitate the optimization process. Results from extensive experiments on industrial cell libraries demonstrate that our method reduces 30/spl times/ fitting error over a well-developed posynomial fitting algorithm. Its application to circuit tuning is also presented.
8794069
"Take One Get One Free": Leveraging P2P Networks for Content Promotion
The nature of digital content is undergoing a radical transformation due to the growing infusion of user-generated content. Users that generate content have a strong motivation to actively promote their content in order to achieve publicity, recognition or to simply spread their viewpoints, knowledge and creations. Existing content sharing mechanisms available to such users, such as in P2P file sharing, are pull-based, relegating the user to a passive role. To enable active participation from users, we propose Promoter, a framework that enhances P2P file sharing systems to support content promotion in addition to the available pull-based content search. Promotion is enabled by publicizing a user's content to peers and disseminating the content by incentivizing peers to download and further propagate it.