Recent papers have indicated that indexing is a promising approach to fast model-based object recognition became it allows most of the possible matches between sets of image features and sets of model features to be q...
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Recent papers have indicated that indexing is a promising approach to fast model-based object recognition became it allows most of the possible matches between sets of image features and sets of model features to be quickly eliminated from consideration. This correspondence describes a system that is capable of indexing using sets of three points undergoing three-dimensional transformations and projection by taking advantage of the probabilistic peaking effect. To be able to index using sets of three points, we must allow false negatives. These are overcome by ensuring that we examine several correct hypotheses. The use of these techniques to speed up the alignment method is described. Results are Even on real and synthetic data.
The Hough Transform for straight line detection is considered. It is shown that if just a small subset of the edge points in the image, selected at random, is used as input for the Hough Transform, the performance is ...
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The Hough Transform for straight line detection is considered. It is shown that if just a small subset of the edge points in the image, selected at random, is used as input for the Hough Transform, the performance is often only slightly impaired, thus the execution time can be considerably shortened. The performance of the resulting "probabilistic Hough Transform" is analysed. The analysis is supported by experimental evidence.
LetRbe random polynomial-time complexity class andUPbe the class of sets accepted by polynomial-time NTMs which have an unique accepting path for each string they accept. LetR(A) be the relativization of these classes...
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LetRbe random polynomial-time complexity class andUPbe the class of sets accepted by polynomial-time NTMs which have an unique accepting path for each string they accept. LetR(A) be the relativization of these classes with respect to oracle A. Similarly, we define co-NP(A)NP(A)UP(A)P(A), and co-R(A). In this paper, we prove the following main results:1. There exists a recursive oracle setAsuch thatR(A) contains aP(A)-immune set.2. There exists a recursive oracle setBsuch thatR(B) contains aP(B)-immune set andNP(B) =R(B).3. There exists an oracle setCsuch thatNP(C) contains aR(C)-immune set andR(C) =P(C).4. There exists a resursive oracle setDsuch thatR(D) contains a simple set andNP(D) =R(D). We have the similar results forUPandZPP=R∩ co-R.
We analyze a probabilistic algorithm for matching shapes modeled by planar regions under translations and rigid motions (rotation and translation). Given shapes A and B, the algorithm computes a transformation t such ...
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We analyze a probabilistic algorithm for matching shapes modeled by planar regions under translations and rigid motions (rotation and translation). Given shapes A and B, the algorithm computes a transformation t such that with high probability the area of overlap of t(A) and B is close to maximal. In the case of polygons, we give a time bound that does not depend significantly on the number of vertices, but on perimeter and area of the shapes and, in the case of rigid motions, also on the diameter. (C) 2009 Elsevier B.V. All rights reserved.
The question of how much time speedup can be achieved by permitting an algorithm to contain probabilistic choices and a small probability of error is considered. It is proved that, for a particular problem determinin...
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The question of how much time speedup can be achieved by permitting an algorithm to contain probabilistic choices and a small probability of error is considered. It is proved that, for a particular problem determining whether 2 sets are disjoint, the speedup is limited to a constant. A probabilistic lower bound is proved. This result extends the probabilistic lower bound result for the case of equal size sets and corrects an old error in the literature. A model of computation based on decision trees is used; the proof techniques are very similar to those introduced by Manber and Tompa (1982).
probabilistic algorithms to evaluate result reliability in qualitativechromatographic analysis are discussed in the paper. The elementary uncertainty (P0), concerned witha single test (comparison of sample and referen...
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probabilistic algorithms to evaluate result reliability in qualitativechromatographic analysis are discussed in the paper. The elementary uncertainty (P0), concerned witha single test (comparison of sample and reference peak positions), is treated as the sum ofmisidentification and omission probabilities. Both constituents are calculated separately using thesimplified model and Laplace functions. In the model, the main reasons for elementary uncertaintiesare random normally distributed deviations during retention characteristic measurement. algorithmsto calculate both constituents of P_0 have to take into account real measurement precision, supposedcomposition of the sample, content of the database, chosen coincidence criterion and other *** a high selectivity of retention, the 3σ value is recommended as the most convenient coincidencecriterion. It leads to more reliable and unambiguous attribution of peaks in the chromatogram. Forcases that are more complicated, the probabilistic algorithms based upon Bernoulli theorem areproposed to calculate the summary uncertainty of identification, concerned with the multiple *** take into account P_0 value, the number of repeated single tests (n) in the similar ordifferent conditions, and chosen identification criterion K (minimal number of coincidences). Theabove-mentioned algorithms lead to a priori optimisation of the mode of operation of anyidentification software system associated with the chromatograph. They can be useful during ametrological validation of corresponding qualitative analysis methods.
A probabilistic Turing machine is a Turing machine with the ability to make decisions based on the outcomes of unbiased coin tosses. The partial function computed by a probabilistic machine is defined by assigning to ...
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A probabilistic Turing machine is a Turing machine with the ability to make decisions based on the outcomes of unbiased coin tosses. The partial function computed by a probabilistic machine is defined by assigning to each input the output which occurs with probability greater than $\fracprobabilisticprobabilistic$. With this definition, only partial recursive functions are probabilistically computable. The run time and tape of probabilistic machines are defined. A palindrome-like language is described that can be recognized faster by one-tape probabilistic Turing machines than by one-tape deterministic Turing machines. It is shown that every nondeterministic machine can be simulated in the same space by a probabilistic machine with small error probability. Several classes of languages recognized probabilistically in polynomial time are defined and compared with $NP$.
This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary probabilistic Structured Grammatical Evolution (Co-PSGE). In Co-PSGE each individual in the population is composed by a ...
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ISBN:
(纸本)9781450392372
This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary probabilistic Structured Grammatical Evolution (Co-PSGE). In Co-PSGE each individual in the population is composed by a grammar and a genotype, which is a list of dynamic lists, each corresponding to a non-terminal of the grammar containing real numbers that correspond to the probability of choosing a derivation rule. Each individual uses its own grammar to map the genotype into a program. During the evolutionary process, both the grammar and the genotype are subject to variation operators. The performance of the proposed approach is compared to 3 different methods, namely, Grammatical Evolution (GE), probabilistic Grammatical Evolution (PGE), and SGE on four different benchmark problems. The results show the effectiveness of the approach since Co-PSGE is able to outperform all the methods with statistically significant differences in the majority of the problems.
In this paper, we introduce probabilistic snap-stabilization. We relax the definition of deterministic snap-stabilization without compromising its safety guarantees. In an unsafe environment, a probabilistically snap-...
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ISBN:
(纸本)9783642452482;9783642452499
In this paper, we introduce probabilistic snap-stabilization. We relax the definition of deterministic snap-stabilization without compromising its safety guarantees. In an unsafe environment, a probabilistically snap-stabilizing algorithm satisfies its safety property immediately after the last fault;whereas its liveness property is only ensured with probability 1. We show that probabilistic snap-stabilization is more expressive than its deterministic counterpart. Indeed, we propose two probabilistic snap-stabilizing algorithms for a problem having no deterministic snap-or self-stabilizing solution: guaranteed service leader election in arbitrary anonymous networks. This problem consists in computing a correct answer to each process that initiates the question "Am I the leader of the network?", i.e., (1) processes always computed the same answer to that question and (2) exactly one process computes the answer true. Our solutions being probabilistically snap-stabilizing, the answers are only delivered within an almost surely finite time;however any delivered answer is correct, regardless the arbitrary initial configuration and provided the question has been properly started.
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