In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as well as possible. As sets of a...
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In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as well as possible. As sets of allowed transformation to match shapes we consider translations, rigid motions, and similarities. We present a generic probabilistic algorithm based on random sampling for matching shapes which are modelled by sets of curves. The algorithm is applicable to the three considered classes of transformations. We analyze which similarity measure is optimized by the algorithm and give rigorous bounds on the number of samples necessary to get a prespecified approximation to the optimal match within a prespecified probability. (C) 2010 Elsevier B.V. All rights reserved.
A recent probabilistic approach for searching in high dimensional metric spaces is based on predicting the distances between database elements according to how they order their distances towards some set of distinguis...
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ISBN:
(纸本)9780769537658
A recent probabilistic approach for searching in high dimensional metric spaces is based on predicting the distances between database elements according to how they order their distances towards some set of distinguished elements, called permutants. In the preprocessing phase a set of permutants is chosen, and are sorted (permuted) by their distances against every database element. The permutations form the index. When a query is given, its corresponding permutation is computed, and as similar elements will (probably) have a similar permutation the database is compared in the order induced by the similarity between permutations. This works well but has relatively high CPU time due to computing the distances between permutations and (partially) sorting the database by the similarity. We improve this by identifying and solving this as another metric space problem. This avoids many distance computations between the permutants. The experimental results show that this works extremely well in practice.
The Lucas primality test is a procedure that verifies an odd integer n for primality using the condition Fn-e(n) equivalent to 0 ( mod n), where {F-n}(n) is the Fibonacci series, e(n) = ((5) (n)) is the Legendre symbo...
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The Lucas primality test is a procedure that verifies an odd integer n for primality using the condition Fn-e(n) equivalent to 0 ( mod n), where {F-n}(n) is the Fibonacci series, e(n) = ((5) (n)) is the Legendre symbol. In this paper, we study a modified Lucas test called the Lucas-Miller-Rabin primality test (briefly, LMR test) and demonstrate that the LMR test is much more efficient than the original Lucas test. We describe necessary and sufficient conditions for composite integers to pass the LMRtest. Composite integers passing it are called LMR-pseudoprimes. We show that the existence of LMR-pseudoprimes divisible by squares depends on the validity of theWall-Sun-Sun Hypothesis, one of the long-standing problems in the Number Theory.
A new method for tracking multiple objects in an intelligent space is proposed in this paper. The observation model is based on a camera ring statically mounted at the ceiling of the environment in order to obtain all...
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ISBN:
(纸本)9781424450589
A new method for tracking multiple objects in an intelligent space is proposed in this paper. The observation model is based on a camera ring statically mounted at the ceiling of the environment in order to obtain all relevant information related to the different objects that wonder (get into and go out) in the space of interest. In the paper, the two subsystems used to track all static and dynamic entities wondering in the intelligent space: a three-dimensional reconstruction of these entities;and, an individual track of all these entities in their movement along the environment with probabilistic techniques. The reliability, and robustness of the proposal presented is finally also demonstrated in this paper with different tests.
In this paper we present a combined opinion recognition scheme based on discriminative algorithms, decision trees and probabilistic algorithms. The proposed scheme takes advantage of the information provided from each...
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ISBN:
(纸本)9783319231327;9783319231310
In this paper we present a combined opinion recognition scheme based on discriminative algorithms, decision trees and probabilistic algorithms. The proposed scheme takes advantage of the information provided from each of the recognition models in decision level, in order to provide refined and more accurate opinion recognition results. The experimental results showed that the proposed combined scheme achieved an overall recognition performance of 87.90 %, increasing the accuracy of our best-performing opinion recognition model by 3.5%.
In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as good as possible. As sets of a...
详细信息
ISBN:
(纸本)9783642002014
In order to determine the similarity between two planar shapes, which is an important problem in computer vision and pattern recognition, it is necessary to first match the two shapes as good as possible. As sets of allowed transformation to match shapes we consider translations, rigid motions, and similarities. We present a generic probabilistic algorithm based on random sampling for matching shapes which are modelled by sets of curves. The algorithm is applicable to the three considered classes of transformations. We analyze which similarity measure is optimized by the algorithm and give rigorous bounds on the number of samples necessary to get a prespecified approximation to the optimal match within a prespecified probability.
Much of the research in Information Retrieval (IR) is devoted to studying the improvement of personalized results for specific users in a static environment. Nevertheless, few approaches take advantage of collective p...
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ISBN:
(纸本)9781450371797
Much of the research in Information Retrieval (IR) is devoted to studying the improvement of personalized results for specific users in a static environment. Nevertheless, few approaches take advantage of collective past searches in a dynamic context where the number of documents is increased according with the passage of time. In this paper, we present an on-line probabilistic algorithm, which uses the collective past searches in a dynamic context to answer static and dynamic queries. Several experiments were carried out with the aim of evaluating the effectiveness of our algorithm. The algorithm's results were compared with the cosine measure. Following the Cranfield paradigm, simulated datasets were used in the experiments. Final results show that it is possible to improve effectiveness in a dynamic context.
We propose a pseudo-primality test using cyclic extensions of Z/nZ. For every positive integer k <= log n, this test achieves the security of k Miller-Rabin tests at the cost of k(1/2+o(1)) Miller-Rabin tests.
We propose a pseudo-primality test using cyclic extensions of Z/nZ. For every positive integer k <= log n, this test achieves the security of k Miller-Rabin tests at the cost of k(1/2+o(1)) Miller-Rabin tests.
We present a novel multi-resolution point sample rendering algorithm for keyframe animations. The algorithm accepts triangle meshes of arbitrary topology as input which are animated by specifying different sets of ver...
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We present a novel multi-resolution point sample rendering algorithm for keyframe animations. The algorithm accepts triangle meshes of arbitrary topology as input which are animated by specifying different sets of vertices at keyframe positions. A multi-resolution representation consisting of prefiltered point samples and triangles is built to represent the animated mesh at different levels of detail. We introduce a novel sampling and stratification algorithm to efficiently generate suitable point sample sets,for moving triangle meshes. Experimental results demonstrate that the new data structure can be used to render highly complex keyframe animations like crowd scenes in real-time.
The list-labeling problem captures the basic task of storing a dynamically changing set of up to n elements in sorted order in an array of size m = (1+ Theta(1))n. The goal is to support insertions and deletions while...
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ISBN:
(纸本)9798331516758;9798331516741
The list-labeling problem captures the basic task of storing a dynamically changing set of up to n elements in sorted order in an array of size m = (1+ Theta(1))n. The goal is to support insertions and deletions while moving around elements within the array as little as possible. Until recently, the best known upper bound stood at O(log(2) n) amortized cost. This bound, which was first established in 1981, was finally improved two years ago, when a randomized O(log(3/2) n) expected-cost algorithm was discovered. The best randomized lower bound for this problem remains O(log n), and closing this gap is considered to be a major open problem in data structures. In this paper, we present the See-Saw Algorithm, a randomized list-labeling solution that achieves a nearly optimal bound of O(log n polyloglog n) amortized expected cost. This bound is achieved despite at least three lower bounds showing that this type of result is impossible for large classes of solutions.
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