Enhancing automatic term recognition algorithms with HTML tags processing

2011 ◽  
Author(s):  
Milan Lučanský ◽  
Marián Šimko ◽  
Mária Bieliková
Keyword(s):  
Recognition Algorithms ◽  
Automatic Term Recognition

scholarly journals Advances in Pattern Recognition Algorithms, Architectures, and Devices

2004 ◽  
Vol 43 (8) ◽  
pp. 1702 ◽  
Author(s):  
Mohammad A. Karim
Keyword(s):  
Pattern Recognition ◽  
Recognition Algorithms

Large-vocabulary speech recognition algorithms

Computer ◽  
2002 ◽  
Vol 35 (3) ◽  
pp. 42-50 ◽  
Author(s):  
M. Padmanabhan ◽  
M. Picheny
Keyword(s):  
Speech Recognition ◽  
Large Vocabulary ◽  
Recognition Algorithms ◽  
Large Vocabulary Speech Recognition

Parallel Improvement for a Classical Template Serial Thinning Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 1509-1513 ◽  
Author(s):  
Mei Xiu Lu ◽  
Fu Rong Wang ◽  
Feng Li
Keyword(s):  
Mathematical Morphology ◽  
Fingerprint Recognition ◽  
Thinning Algorithm ◽  
Recognition Algorithms ◽  
Characteristic Points ◽  
Image Thinning ◽  
Ideal Algorithm ◽  
Improved Algorithm

Image thinning is one of important steps of fingerprint preprocessing. Most of fingerprint recognition algorithms checked the characteristic points on thinning image. In this paper, we discover some shortages in OPTA and mathematical morphology thinning algorithm and find out the reasons for some shortages such as many glitches and snags, defective thinning, and so on. A new improved algorithm is proposed in the paper, which is an ideal algorithm because it is faster, produces less glitch, and thins completely.

Application of image enhancement and pattern recognition algorithms to seismic reflection data

1983 ◽  
Vol 73 (1) ◽  
pp. 307-314
Author(s):  
George A. McMechan
Keyword(s):  
Pattern Recognition ◽  
Pacific Ocean ◽  
Image Enhancement ◽  
Seismic Reflection ◽  
Gray Scale ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Recognition Algorithms ◽  
Gray Scale Image ◽  
Northeastern Pacific

abstract A digital seismic reflection section may be converted to a gray scale image composed of pixels and processed with techniques borrowed from the disciplines of image enhancement and pattern recognition. Types of processing include scaling, thresholding, density equalization, filtering, segmentation, and edge-finding. These are successfully applied to a migrated common mid-point seismic reflection line that traverses the Queen Charlotte fault (located in the northeastern Pacific Ocean). The result is the definition and enhancement of an elongated, near-vertical reflectivity anomaly associated with the Queen Charlotte fault.

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