Pattern recognition technique

Technique operates regardless of pattern rotation, translation or magnification and successfully detects out-of-register patterns. It improves accuracy and reduces cost of various optical character recognition devices and page readers and provides data input to computer

Development of Transformations from Business Process Models to Implementations by Reuse

This paper presents an approach for developing transformations from business process models to implementations that facilitates reuse. A transformation is developed as a composition of three smaller tasks: pattern recognition, pattern realization and activity transformation. The approach allows one to reuse the definition and implementation of pattern recognition and pattern realization in the development of transformations targeting different business process modeling and implementation languages. In order to decouple pattern recognition and pattern realization, the approach includes a pattern language to represent the output of the pattern recognition task, which forms the input of the pattern realization task

Metrizability and pattern recognition

Metrizability and pattern recognitio

Pattern Recognition In Non-Kolmogorovian Structures

We present a generalization of the problem of pattern recognition to arbitrary probabilistic models. This version deals with the problem of recognizing an individual pattern among a family of different species or classes of objects which obey probabilistic laws which do not comply with Kolmogorov's axioms. We show that such a scenario accommodates many important examples, and in particular, we provide a rigorous definition of the classical and the quantum pattern recognition problems, respectively. Our framework allows for the introduction of non-trivial correlations (as entanglement or discord) between the different species involved, opening the door to a new way of harnessing these physical resources for solving pattern recognition problems. Finally, we present some examples and discuss the computational complexity of the quantum pattern recognition problem, showing that the most important quantum computation algorithms can be described as non-Kolmogorovian pattern recognition problems

Fault tolerant parallel pattern recognition

The general capabilities of fault tolerant computations in one-way and two-way linear cellular arrays are investigated in terms of pattern recognition. The defective processing elements (cells) that cause the misoperations are assumed to behave as follows. Dependent on the result of a self-diagnosis they store their working state locally such that it becomes visible to the neighbors. A non-working (defective) cell cannot modify information but is able to transmit it unchanged with unit speed. Arrays with static defects run the self-diagnosis once before the actual computation. Subsequently no more defects may occur.In case of dynamic defects cells may fail during the computation. We center our attention to patterns that are recognizable very fast, i.e. in real-time, but almost all results can be generalized to arbitrary recognition times in a straightforward manner. It is shown that fault tolerant recognition capabilities of two-way arrays with static defects are characterizable by intact one-way arrays and that one-way arrays are fault tolerant per se. For arrays with dynamic defects it is proved that the failures can be compensated as long as the number of adjacent defective cells is bounded. Arbitrary large defective regions (and thus fault tolerant computations) lead to a dramatically decrease of computing power. The recognizable patterns are those of a single processing element, the regular ones. CR Subject Classification (1998): F.1, F.4.3, B.6.1, E.1, B.8.1, C.

Stochastic-Based Pattern Recognition Analysis

In this work we review the basic principles of stochastic logic and propose its application to probabilistic-based pattern-recognition analysis. The proposed technique is intrinsically a parallel comparison of input data to various pre-stored categories using Bayesian techniques. We design smart pulse-based stochastic-logic blocks to provide an efficient pattern recognition analysis. The proposed rchitecture is applied to a specific navigation problem. The resulting system is orders of magnitude faster than processor-based solutions

Pattern recognition on a quantum computer

By means of a simple example it is demonstrated that the task of finding and identifying certain patterns in an otherwise (macroscopically) unstructured picture (data set) can be accomplished efficiently by a quantum computer. Employing the powerful tool of the quantum Fourier transform the proposed quantum algorithm exhibits an exponential speed-up in comparison with its classical counterpart. The digital representation also results in a significantly higher accuracy than the method of optical filtering. PACS: 03.67.Lx, 03.67.-a, 42.30.Sy, 89.70.+c.Comment: 6 pages RevTeX, 1 figure, several correction

On sample complexity for computational pattern recognition

In statistical setting of the pattern recognition problem the number of examples required to approximate an unknown labelling function is linear in the VC dimension of the target learning class. In this work we consider the question whether such bounds exist if we restrict our attention to computable pattern recognition methods, assuming that the unknown labelling function is also computable. We find that in this case the number of examples required for a computable method to approximate the labelling function not only is not linear, but grows faster (in the VC dimension of the class) than any computable function. No time or space constraints are put on the predictors or target functions; the only resource we consider is the training examples. The task of pattern recognition is considered in conjunction with another learning problem -- data compression. An impossibility result for the task of data compression allows us to estimate the sample complexity for pattern recognition

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201