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

scholarly journals Detection and Classification of Vehicles on Curved Roads

2019 ◽  
Vol 8 (10S) ◽  
pp. 152-156
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Moving Object Detection ◽  
Morphological Operations ◽  
Moving Vehicles ◽  
Blob Analysis

Detection of a vehicle is a very important aspect for traffic monitoring. It is based on the concept of moving object detection. Classifying the detected object as vehicle and class of vehicle is also having application in various application domains. This paper aims at providing an application of vehicle detection and classification concept to detect vehicles along curved roads in Indian scenarios. The main purpose is to ensure safety in such roads. Gaussian mixture model and blob analysis are the methods applied for the detection of vehicles. Morphological operations are used to eliminate noise. The moving vehicles are detected and the class of the vehicle is identified.

Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography

2020 ◽  
Vol 10 (5) ◽  
pp. 1033-1039
Author(s):  
Huihong Duan ◽  
Xu Wang ◽  
Xingyi He ◽  
Yonggang He ◽  
Litao Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Pulmonary Nodules ◽  
Gaussian Mixture ◽  
Local Optimum ◽  
Computer Aided ◽  
Aided Diagnosis

Background: In the pulmonary nodules computer aided diagnosis systems (CAD), feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of features was damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonary nodules. Method: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM). Secondly, we applied Relief and sequential forward selection (SFS) algorithm to find local optimum features dataset for each group. Afterwards, we used the optimumpath forest (OPF) classifier with the found features dataset to obtain the classification results. Finally, the local optimum features dataset with the highest area under curve AUC in all groups were added into the final selected set. Results: According to collected pulmonary nodules on computed tomography (CT) scans, tested with two set of samples, we achieved an average accuracy of 89.5%, sensitivity of 87.1% and specificity of 90.9% on the first set of samples, and 90.1%, 88.7% and 92.1% on the second set of samples. The areas under the receiver operating characteristic (ROC) curves based on these two sample sets were 95.2%, and 96.3% respectively. Conclusions: This study shows that the proposed method was promising for improving the pulmonary nodules computer aided diagnosis systems performance of benign and malignant pulmonary nodules.

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.

Gaussian Mixture Model and Classification of Polarimetric Features for SAR Images

2019 ◽  
Vol 39 (1) ◽  
pp. 0128002
Author(s):  
李珞茹 Li Luoru ◽  
徐新 Xu Xin ◽  
董浩 Dong Hao ◽  
桂容 Gui Rong ◽  
谢欣芳 Xie Xinfang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Sar Images
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