Abstract

The output of boolean association rule mining algorithms is often too large for manual examination. For dense datasets, it is often impractical to even generate all frequent itemsets. The closed itemset approach handles this information overload by pruning “uninteresting” rules following the observation that most rules can be derived from other rules. In this paper, we propose a new framework, namely, the generalized closed (or
-closed) itemset framework. By allowing for a small tolerance in the accuracy of itemset supports, we show that the number of such redundant rules is far more than what was previously estimated. Our scheme can be integrated into both levelwise algorithms (Apriori) and two-pass algorithms (ARMOR). We evaluate its performance by measuring the reduction in output size as well as in response time. Our experiments show that incorporating g-closed itemsets provides significant performance improvements on a variety of databases.

Abstract

In this paper, we first focus our attention on the question of how much space remains for performance improvement over current association rule mining algorithms. Our strategy is to compare their performance against an “Oracle algorithm” that knows in advance the identities of all frequent itemsets in the database and only needs to gather their actual supports to complete the mining process. Our experimental results show that current mining algorithms do not perform uniformly well with respect to the Oracle for all database characteristics and support thresholds. In many cases there is a substantial gap between the Oracle’s performance and that of the current mining algorithms. Second, we present a new mining algorithm, called ARMOR, that is constructed by making minimal changes to the Oracle algorithm. ARMOR consistently performs within a factor of two of the Oracle on both real and synthetic datasets over practical ranges of support specifications.

Abstract

Most natural spatiotemporal systems are an organized ensemble of dynamical subsystems whose behaviour is regulated by nonlinearly coupled multi-variable processes. The response of each of these variables and/or combinations of them to any single or composite external perturbation can influence its spatiotemporal behaviour in a complex and non-intuitive manner. This paper attempts to study the dynamical response of two coupled map lattice systems having local dynamics described by (a) coupled discrete maps, and (b) coupled differential equations, to external perturbation (or “pinning”) applied to each variable individually or simultaneously. We show that the response of different variables to external pinning is quite different. Our results indicate that, though this pinning approach is useful in controlling complex dynamics both globally and locally, enhancing complexity in dynamics (“anti-control”) is …

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Abstract

Multiview analysis of scenes includes the study of sceneindependent constraints satisfied by a configuration of cameras for all types of scenes as well as the study of viewindependent constraints satisfied by any camera on a configuration of points. In this paper, we derive new constraints involving configurations of points that move with constant velocity, with constant acceleration, and for unconstrained planar motion. We show how these constraints can be applied to problems like motion recognition, frame alignment, etc.

Abstract

Polygon approximation is an important step in the recognition of planar shapes. Traditional polygonal approximation algorithms handle only images that are related by a similarity transformation. The transformation of a planar shape as the viewpoint changes with a perspective camera is a general projective one. In this paper, we present a novel method for polygonal approximation of closed curves that is invariant to projective transformation. The polygons generated by our algorithm from two images, related by a projective homography, are isomorphic. We also describe an application of this in the form of numeral recognition. We demonstrate the importance of this algorithm for real-life applications like number plate recognition, aircraft recognition and metric rectification.

Abstract

Correspondence between pixels is an important problem in stereo vision. Several algorithms have been proposed to carry out this task in literature. Almost all of them employ only gray-values. We show here that addition of primary or secondary evidence maps can improve the correspondence computation. However any particular combination is not guaranteed to provide proper results in a general sitiuation. What one needs is a mechanism to select the evidences which are apropriate for a particular pair of images. We present an algorithm for stereo correspondence that can take advantage of different image features adaptively for matching. A match measure combining different individual measures computed from different featuresis used by our algorithm. The advantages of each feature can be combined in a single correspondence computation. We describe an unsupervised scheme to compute the relevance of each feature to a particular situation, given a set of possibly useful features. We present an implementation of the scheme using dynamic programming for pixel-to-pixel correspondence

Abstract

We present an algorithm for stereo correspondence that can take advantage of different image features adaptively for matching. A match measure combining different match measures computed from different features is used by our algorithm. It is possible to compute correspondences using the gray value, multispectral components, derived features such as the edge strength, texture, etc., in a flexible manner using this algorithm. The advantages of each feature can be combined in a single correspondence computation. We describe a non-supervised scheme to compute the relevance of each feature to a particular situation, given a set of possibly useful features. We present an implementation of the scheme using dynamic programming for pixel-to-pixel correspondence. Results demonstrate the advantages of our scheme under different conditions.

Abstract

Computing correspondences between pairs of images is fundamental to all structures from motion algorithms. Correlation is a popular method to estimate similarity between patches of images. In the standard formulation, the correlation function uses only one feature such as the gray level values of a small neighbourhood. Research has shown that different features—such as colour, edge strength, corners, texture measures—work better under di0erent conditions. We propose a framework of generalized correlation that can compute a real valued similarity measure using a feature vector whose components can be dissimilar. The framework can combine the e0ects of di0erent image features, such as multi-spectral features, edges, corners, texture measures, etc., into a single similarity measure in a 3exible manner. Additionally, it can combine results of di0erent window sizes used for correlation with proper weighting for each. Relative importances of the features can be estimated from the image itself for accurate correspondence. In this paper, we present the framework of generalised correlation, provide a few examples demonstrating its power, as well as discuss the implementation issues. ? 2002 Published by Elsevier Science Ltd on behalf of Pattern Recognition Society.