A generalized interpolative vector quantization method for jointly optimal quantization, interpolation, and binarization of text images

2000 ◽  
Vol 9 (7) ◽  
pp. 1272-1281 ◽  
Author(s):  
F. Fekri ◽  
R.M. Mersereau ◽  
R.W. Schafer
Keyword(s):  
Vector Quantization ◽  
Quantization Method ◽  
Optimal Quantization ◽  
Text Images

scholarly journals USING LEARNING VECTOR QUANTIZATION METHOD FOR AUTOMATED IDENTIFICATION OF MYCOBACTERIUM TUBERCULOSIS

2015 ◽  
Vol 3 (1) ◽  
pp. 26 ◽  
Author(s):  
Endah Purwanti ◽  
Prihartini Widiyanti
Keyword(s):  
Image Processing ◽  
Mycobacterium Tuberculosis ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Quantization Method ◽  
Automated Identification ◽  
Clinical Specimens ◽  
Automated Method

In this paper, we are developing an automated method for the detection of tubercle bacilli in clinical specimens, principally the sputum. This investigation is the first attempt to automatically identify TB bacilli in sputum using image processing and learning vector quantization (LVQ) techniques. The evaluation of the learning vector quantization (LVQ) was carried out on Tuberculosis dataset show that average of accuracy is 91,33%.

A Method to Estimate the Multimedia Communication Bands Based upon Multi-Order Markov Model

2007 ◽  
Vol 11 (6) ◽  
pp. 655-661
Author(s):  
Tetsuya Kojima ◽  
◽  
Lkhamsuren Enkhtur ◽  
Akiko Fujiwara ◽  
Masahiro Aono ◽  
...  
Keyword(s):  
Markov Model ◽  
Multimedia Communication ◽  
Streaming Video ◽  
Video Data ◽  
Data Streaming ◽  
Quantization Method ◽  
Future Studies ◽  
Transmission Rates ◽  
Optimal Quantization ◽  
Total Data

Surplus bands inevitably occur in the multimedia communication bands when VBR encoding is used for data streaming. These bands can be used efficiently by transmitting the other data such as a kind of additive static contents at the same time. However, there are some delays for adding the data, so that the transmission rates of the total data frequently exceed the maximum communication band. This is because each surplus bandwidth is calculated from the formerly observed data. We have proposed a method to estimate the surplus bandwidth by using the multi-order Markov model together with the quantization of the bandwidth. In this paper, we investigate the optimal quantization method for a given streaming video data. In addition, the effectiveness of the proposed method is evaluated under the optimal quantization settings. Discussions for some problems in future studies are also included.

scholarly journals Automatic Speaker Identification by Voice Based on Vector Quantization Method

2019 ◽  
Vol 8 (10) ◽  
pp. 2443-2445
Keyword(s):  
Feature Extraction ◽  
Vector Quantization ◽  
Speaker Identification ◽  
Voice Recognition ◽  
Recognition System ◽  
Automatic Identification ◽  
Quantization Method ◽  
Feature Vectors ◽  
Cepstral Features ◽  
Code Book

In this paper, the systems of speaker identification of a text-dependent and independent nature were considered. Feature extraction was performed using chalk-frequency cepstral coefficients (MFCC). The vector quantization method for the automatic identification of a person by voice has been investigated. Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Speakers were modeled using vector quantization (VQ). Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Codebooks of all announcers were collected in the database. From the results, it can be said that vector quantization using cepstral features produces good results for creating a voice recognition system.

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