Single frame IR point target detection based on a Gaussian mixture model classification

2012 ◽  
Author(s):  
Laure Genin ◽  
Frédéric Champagnat ◽  
Guy Le Besnerais
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Target Detection ◽  
Gaussian Mixture ◽  
Model Classification ◽  
Point Target ◽  
Single Frame ◽  
Point Target Detection

scholarly journals Comparison of Parametric representations of Birdcall in Gaussian Mixture model

2017 ◽  
Vol 2 (3) ◽  
pp. 124-130
Author(s):  
Ricky Mohanty ◽  
Sandeep Singh Solanki
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Bird Species ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Extraction Methods ◽  
Paper Briefly ◽  
Mel Frequency Cepstral Coefficients ◽  
Model Classification ◽  
Audio Recordings

This paper focuses on the methods of automatic classifications of birds into different species based on feature extraction methods & audio recordings of their sounds. The recognition system uses Gaussian mixture model (GMM) to model 14 poultry bird species calls. Mel frequency cepstral coefficients (MFCC) parameters & wavelet parameters are used for feature vector extraction. The paper briefly explains the methods &  also evaluates the performance of these methods in Gaussian Mixture Model classification .The results depicts the performance of  Gaussian Mixture Model classification using wavelet was more efficient in terms of percentage of accuracy  at around 80% and computation was also faster.

scholarly journals Gaussian mixture model classification of odontocetes in the Southern California Bight and the Gulf of California

2007 ◽  
Vol 121 (3) ◽  
pp. 1737-1748 ◽  
Author(s):  
Marie A. Roch ◽  
Melissa S. Soldevilla ◽  
Jessica C. Burtenshaw ◽  
E. Elizabeth Henderson ◽  
John A. Hildebrand
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gulf Of California ◽  
Southern California ◽  
Gaussian Mixture ◽  
Southern California Bight ◽  
Model Classification
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