A NEW KERNEL-BASED FORMALIZATION OF MINIMUM ERROR PATTERN RECOGNITION

2005 ◽  
pp. 41-54
Author(s):  
Erik McDermott ◽  
Shigeru Katagiri
Keyword(s):  
Pattern Recognition ◽  
Error Pattern ◽  
Minimum Error

Models for neurovestibular adaptation

2003 ◽  
Vol 13 (4-6) ◽  
pp. 297-307 ◽  
Author(s):  
Laurence R. Young
Keyword(s):  
Pattern Recognition ◽  
Adaptive Control ◽  
Dynamic Models ◽  
Sensor Integration ◽  
Error Pattern ◽  
Model Reference ◽  
Input And Output

Dynamic models have played a more prominent role in the vestibular and oculomotor field than in any other branch of physiology. The ease of identification of input and output variables and the challenge of multi-loop, multi-axis adaptive control has attracted numerous modelers from engineering and shaped behavioral and neurophysiological experimental programs. In particular, the adaptive characteristics of the neurovestibular system have generated continuing speculation as to mechanisms. This treatment of adaptation and multi-sensor integration covers the development and application of such models, principally in the author's laboratory. It emphasizes the continuing relevance of both "model reference" and "error pattern recognition" notions of adaptive control.

A MINIMUM ERROR RATE PATTERN RECOGNITION APPROACH TO SPEECH RECOGNITION

1994 ◽  
Vol 08 (01) ◽  
pp. 5-31 ◽  
Author(s):  
W. CHOU ◽  
C.-H. LEE ◽  
B.-H. JUANG ◽  
F.K. SOONG
Keyword(s):  
Pattern Recognition ◽  
Speech Recognition ◽  
Error Rate ◽  
Training Algorithms ◽  
Generation Process ◽  
Viterbi Decoding ◽  
Continuous Speech Recognition ◽  
Minimum Error ◽  
Pattern Recognition Approach ◽  
Speech Recognizer

In this paper, a minimum error rate pattern recognition approach to speech recognition is studied with particular emphasis on the speech recognizer designs based on hidden Markov models (HMMs) and Viterbi decoding. This approach differs from the traditional maximum likelihood based approach in that the objective of the recognition error rate minimization is established through a specially designed loss function, and is not based on the assumptions made about the speech generation process. Various theoretical and practical issues concerning this minimum error rate pattern recognition approach in speech recognition are investigated. The formulation and the algorithmic structures of several minimum error rate training algorithms for an HMM-based speech recognizer are discussed. The tree-trellis based N-best decoding method and a robust speech recognition scheme based on the combined string models are described. This approach can be applied to large vocabulary, continuous speech recognition tasks and to speech recognizers using word or subword based speech recognition units. Various experimental results have shown that significant error rate reduction can be achieved through the proposed approach.

A Novel Weave/Color Repeat Extraction Method with Error Tolerance

2014 ◽  
Vol 9 (2) ◽  
pp. 155892501400900
Author(s):  
Di Zhou ◽  
Luoqing Zhou ◽  
Xinyi Sheng ◽  
Jun Sun
Keyword(s):  
Pattern Recognition ◽  
Efficient Method ◽  
Extraction Method ◽  
Repeat Unit ◽  
Color Pattern ◽  
Error Tolerance ◽  
Error Pattern ◽  
Statistical Solution ◽  
Dyed Fabric ◽  
Weave Pattern

In this paper, we propose a simple but efficient method to identify the correct repeat unit in a yarn-dyed fabric weave pattern and color pattern automatically, especially when they contain error pattern recognitions. With only the color pattern of the fabric to be detected, our method can solve three problems simultaneously, even though there are errors in color pattern recognition. The method extracts the weave/color repeat unit, identifies the full weave pattern, and locates and rectifies the misrecognition in color pattern if there is one. The layout of color yarns is obtained by statistical solution. The weave pattern is partly determined by the correlation between color pattern and weave pattern, while that left can be identified by tentative grouping. In this process, the weave repeat unit is obtained when every group achieves internal uniformity; moreover the misrecognition in color pattern can be located and rectified simultaneously. Finally, the color repeat unit can also be extracted in the same way. Experiments on real fabrics validate the effectiveness of our method in extracting weave/color repeat unit from color pattern which contains errors.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

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