Depth Matching: A Pattern-Matching Problem

1986 ◽  
pp. 41-60
Author(s):  
Mark G. Kerzner
Keyword(s):  
Pattern Matching ◽  
Matching Problem

A fast expected time algorithm for the 2-D point pattern matching problem

2004 ◽  
Vol 37 (8) ◽  
pp. 1699-1711 ◽  
Author(s):  
P.B. Van Wamelen ◽  
Z. Li ◽  
S.S. Iyengar
Keyword(s):  
Pattern Matching ◽  
Time Algorithm ◽  
Point Pattern ◽  
Matching Problem ◽  
Point Pattern Matching ◽  
Expected Time

A new algorithm for the small-field astrometric point-pattern matching problem

2018 ◽  
Vol 72 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Cláudio P. Santiago ◽  
Carlile Lavor ◽  
Sérgio Assunção Monteiro ◽  
Alberto Kroner-Martins
Keyword(s):  
Pattern Matching ◽  
Point Pattern ◽  
Small Field ◽  
Matching Problem ◽  
Point Pattern Matching

scholarly journals Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

2010 ◽  
Vol 20 (1) ◽  
pp. 135-147 ◽  
Author(s):  
Ewa Świercz
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Pattern Matching ◽  
Classification Algorithm ◽  
Statistical Distribution ◽  
Classification Rule ◽  
Matching Problem ◽  
Time Frequency ◽  
Stationary Signals ◽  
Non Stationary Signal

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distributionA new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requiresa prioriknowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to themaxdiscriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

IDPM: An Improved Degenerate Pattern Matching Algorithm for Biological Sequences

2017 ◽  
Vol 28 (07) ◽  
pp. 889-914
Author(s):  
Jie Lin ◽  
Yue Jiang ◽  
E. James Harner ◽  
Bing-Hua Jiang ◽  
Don Adjeroh
Keyword(s):  
Performance Improvement ◽  
Pattern Matching ◽  
Linear Time ◽  
Computational Cost ◽  
Large Data ◽  
Biological Sequences ◽  
Matching Problem ◽  
Practical Utilization ◽  
Matching Algorithm ◽  
Pattern Matching Algorithm

Let [Formula: see text] be a string, with symbols from an alphabet. [Formula: see text] is said to be degenerate if for some positions, say [Formula: see text], [Formula: see text] can contain a subset of symbols from the symbol alphabet, rather than just one symbol. Given a text string [Formula: see text] and a pattern [Formula: see text], both with symbols from an alphabet [Formula: see text], the degenerate string matching problem, is to find positions in [Formula: see text] where [Formula: see text] occured, such that [Formula: see text], [Formula: see text], or both are allowed to be degenerate. Though some algorithms have been proposed, their huge computational cost pose a significant challenge to their practical utilization. In this work, we propose IDPM, an improved degenerate pattern matching algorithm based on an extension of the Boyer–Moore algorithm. At the preprocessing phase, the algorithm defines an alphabet-independent compatibility rule, and computes the shift arrays using respective variants of the bad character and good suffix heuristics. At the search phase, IDPM improves the matching speed by using the compatibility rule. On average, the proposed IDPM algorithm has a linear time complexity with respect to the text size, and to the overall size of the pattern. IDPM demonstrates significance performance improvement over state-of-the-art approaches. It can be used in fast practical degenerate pattern matching with large data sizes, with important applications in flexible and scalable searching of huge biological sequences.

scholarly journals Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoyun Wang ◽  
Xianquan Zhang
Keyword(s):  
Pattern Matching ◽  
Complete Graph ◽  
Point Pattern ◽  
Point Sets ◽  
Matching Problem ◽  
Matching Algorithm ◽  
Planar Point ◽  
Point Pattern Matching ◽  
Point Set ◽  
Pattern Matching Algorithm

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

Parallel Algorithms for the Boxed-Mesh Permutation Pattern Matching Problem

2019 ◽  
Vol 46 (4) ◽  
pp. 299-307
Author(s):  
Jihyo Choi ◽  
Youngho Kim ◽  
Joong Chae Na ◽  
Jeong Seop Sim
Keyword(s):  
Parallel Algorithms ◽  
Pattern Matching ◽  
Matching Problem ◽  
Permutation Pattern

scholarly journals Permutation Pattern matching in (213, 231)-avoiding permutations

2017 ◽  
Vol Vol. 18 no. 2, Permutation... (Permutation Patterns) ◽  
Author(s):  
Both Neou ◽  
Romeo Rizzi ◽  
Stéphane Vialette
Keyword(s):  
Pattern Matching ◽  
Related Problem ◽  
Linear Time ◽  
Time Algorithm ◽  
Linear Time Algorithm ◽  
Matching Problem ◽  
Permutation Pattern ◽  
Special Case ◽  
Longest Subsequence

Given permutations σ of size k and π of size n with k < n, the permutation pattern matching problem is to decide whether σ occurs in π as an order-isomorphic subsequence. We give a linear-time algorithm in case both π and σ avoid the two size-3 permutations 213 and 231. For the special case where only σ avoids 213 and 231, we present a O(max(kn 2 , n 2 log log n)-time algorithm. We extend our research to bivincular patterns that avoid 213 and 231 and present a O(kn 4)-time algorithm. Finally we look at the related problem of the longest subsequence which avoids 213 and 231.

On the relationship between histogram indexing and block-mass indexing

2014 ◽  
Vol 372 (2016) ◽  
pp. 20130132 ◽  
Author(s):  
Amihood Amir ◽  
Ayelet Butman ◽  
Ely Porat
Keyword(s):  
Pattern Matching ◽  
Linear Time ◽  
Matching Problem ◽  
Open Problems ◽  
Time Solution ◽  
Text Length ◽  
Active Research ◽  
Histogram Indexing ◽  
The Relationship ◽  
Mass Pattern

Histogram indexing , also known as jumbled pattern indexing and permutation indexing is one of the important current open problems in pattern matching. It was introduced about 6 years ago and has seen active research since. Yet, to date there is no algorithm that can preprocess a text T in time o (| T | 2 /polylog| T |) and achieve histogram indexing, even over a binary alphabet, in time independent of the text length. The pattern matching version of this problem has a simple linear-time solution. Block-mass pattern matching problem is a recently introduced problem, motivated by issues in mass-spectrometry. It is also an example of a pattern matching problem that has an efficient, almost linear-time solution but whose indexing version is daunting. However, for fixed finite alphabets, there has been progress made. In this paper, a strong connection between the histogram indexing problem and the block-mass pattern indexing problem is shown. The reduction we show between the two problems is amazingly simple. Its value lies in recognizing the connection between these two apparently disparate problems, rather than the complexity of the reduction. In addition, we show that for both these problems, even over unbounded alphabets, there are algorithms that preprocess a text T in time o (| T | 2 /polylog| T |) and enable answering indexing queries in time polynomial in the query length. The contributions of this paper are twofold: (i) we introduce the idea of allowing a trade-off between the preprocessing time and query time of various indexing problems that have been stumbling blocks in the literature. (ii) We take the first step in introducing a class of indexing problems that, we believe, cannot be pre-processed in time o (| T | 2 /polylog| T |) and enable linear-time query processing.

A GPU-BASED ALGORITHM FOR APPROXIMATELY FINDING THE LARGEST COMMON POINT SET IN THE PLANE UNDER SIMILARITY TRANSFORMATION

2009 ◽  
Vol 09 (02) ◽  
pp. 287-298
Author(s):  
DROR AIGER ◽  
KLARA KEDEM
Keyword(s):  
Pattern Matching ◽  
Similarity Transformation ◽  
Common Point ◽  
Geometric Pattern ◽  
Matching Problem ◽  
Real World Applications ◽  
Geometric Pattern Matching ◽  
Point Set ◽  
Directional Hausdorff Distance ◽  
Transformation Space

We consider the following geometric pattern matching problem: Given two sets of points in the plane, P and Q, and some (arbitrary) δ > 0, find the largest subset B ⊂ P and a similarity transformation T (translation, rotation and scale) such that h(T(B),Q) < δ, where h(.,.) is the directional Hausdorff distance. This problem stems from real world applications, where δ is determined by the practical uncertainty in the position of the points (pixels). We reduce the problem to finding the depth (maximally covered point) of an arrangement of polytopes in transformation space. The depth is the cardinality of B, and the polytopes that cover the deepest point correspond to the points in B. We present an algorithm that approximates the maximum depth with high probability, thus getting a large enough common point set in P and Q. The algorithm is implemented in the GPU framework, thus it is very fast in practice. We present experimental results and compare their runtime with those of an algorithm running on the CPU.

Sign in / Sign up

Export Citation Format

Share Document