Face verification using local binary pattern–unconstrained minimum average correlation energy correlation filters

A method to classify the genre of traditional Malay music using spectrogram correlation is described. The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.

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Recent advances to nonlinear minimum average correlation energy filters

Optical Engineering ◽

10.1117/1.601518 ◽

1997 ◽

Vol 36 (10) ◽

pp. 2697 ◽

Cited By ~ 11

Author(s):

John W. Fisher

Keyword(s):

Correlation Energy ◽

Average Correlation ◽

Recent Advances

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Complexity Evaluation Method of Electromagnetic Environment Oriented to Communication Equipment Based on Statistic Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.1331 ◽

2014 ◽

Vol 568-570 ◽

pp. 1331-1335 ◽

Cited By ~ 1

Author(s):

Shao Jun Zhang ◽

Dong Lin Su ◽

Jian Wang ◽

Rui Wang

Keyword(s):

Evaluation Method ◽

Correlation Energy ◽

Subjective Evaluation ◽

Correlation Factor ◽

Electromagnetic Environment ◽

Energy Correlation ◽

Statistical Concept ◽

Working Parameters ◽

Communication Equipment ◽

Polarization Correlation

To improve the objectivity and accuracy of evaluation for electromagnetic environment complexity and its influence on communication equipment, a subjective complexity evaluation method of electromagnetic environment oriented to the communication equipment is presented based on sensitivity and RF protection ratio of communication equipment. Based on statistical concept, the energy correlation factor is modified, and modulation and polarization correlation concept are imported into this method, by which the factor of energy correlation is modified and the subjective evaluation is made by analyzing the time domain correlation, frequency domain correlation, energy domain correlation, modulation correlation and polarization correlation of the electromagnetic environment. These simulation results indicate that communication equipment with various working parameters are influenced differently under the same electromagnetic environment. Namely, the complexities of electromagnetic environment for communication equipment with various working parameters are different.

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Long-term target tracking combined with re-detection

10.21203/rs.3.rs-51036/v3 ◽

2020 ◽

Author(s):

Juanjuan Wang ◽

HaoRan Yang ◽

Ning Xu ◽

Chengqin Wu ◽

ZengShun Zhao ◽

...

Keyword(s):

Correlation Energy ◽

State Of The Art ◽

Tracking Algorithm ◽

Correlation Filters ◽

Short Term ◽

Tracking Method ◽

Tracking Tasks ◽

Svm Model ◽

Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

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