Fast Match Techniques

1996 ◽  
pp. 413-428 ◽  
Author(s):  
P. S. Gopalakrishnan ◽  
L. R. Bahl
Keyword(s):  
Fast Match

Matrix fast match: a fast method for identifying a short list of candidate words for decoding

2003 ◽  
Author(s):  
L. Bahl ◽  
P.S. Gopalakrishnan ◽  
D. Kanevsky ◽  
D. Nahamoo
Keyword(s):  
Fast Method ◽  
Short List ◽  
Fast Match

scholarly journals Effectiveness of n-gram fast match for query-by-humming systems

2009 ◽  
Author(s):  
Jue Hou ◽  
Dan-ning Jiang ◽  
Wen-xiao Cao ◽  
Yong Qin ◽  
Thomas Fang Zheng ◽  
...  
Keyword(s):  
Query By Humming ◽  
N Gram ◽  
Fast Match

scholarly journals Pattern Recognition on Vehicle Number Plates Using a Fast Match Algorithm

2021 ◽  
Vol 6 (2) ◽  
pp. 103-110
Author(s):  
Cahaya Jatmoko ◽  
Daurat Sinaga ◽  
Edi Sugiarto ◽  
Nur Rokhman ◽  
Heru Lestiawan
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Original Image ◽  
Traffic System ◽  
Match Algorithm ◽  
The World ◽  
Fast Match

Computer Vision was the fast developing apps in the world, it is make people make a lot of new algorithm. Before we can use in out app, we need to test the algorithm to make sure how effective and optimal the algorithm to solve every case we given. A lot of traffic system has implemented computer vision, they need fast and can work in every condition, because every vehicle who pass needs to be recognized. In this research Fast Match algorithm was chosen because they can solve some test and make a lot of image have a similarity with the template. It makes accuracy of the data can be achieved with this algorithm. For example on of the sample was have a SAD point for 0.5 and Overlap Error for 0.5 and can run in standard computer just for a couple second. It makes the template and the original image has a little similarity.

STRING MATCHING ARTIFICIAL NEURAL NETWORKS

2001 ◽  
Vol 11 (05) ◽  
pp. 445-453 ◽  
Author(s):  
TATIANA TAMBOURATZIS
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Building Block ◽  
String Matching ◽  
Matching Problems ◽  
Low Computational Complexity ◽  
Artificial Neural ◽  
Harmony Theory ◽  
Fast Match

Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.

scholarly journals Colour fast-match for precise vehicle retrieval

2020 ◽  
Vol 2020 (4) ◽  
pp. 132-139
Author(s):  
Jian Wei ◽  
Yue Wang ◽  
Feng Liu ◽  
Qiuli Lin ◽  
Ning Wang
Keyword(s):  
Fast Match

Fast Match-Based Vector Quantization Partial Discharge Pulse Pattern Recognition

2005 ◽  
Vol 54 (1) ◽  
pp. 3-9 ◽  
Author(s):  
T.K. Abdel-Galil ◽  
Y.G. Hegazy ◽  
M.M.A. Salama ◽  
R. Bartnikas
Keyword(s):  
Pattern Recognition ◽  
Vector Quantization ◽  
Partial Discharge ◽  
Discharge Pulse ◽  
Fast Match
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