A new automatic obstacle detection method based on selective updating of Gaussian mixture model

2015 ◽  
Author(s):  
Jinhui Lan ◽  
Yaoliang Jiang ◽  
Dongyang Yu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Obstacle Detection

An Improved Moving Body Detection Method Based on Gaussian Mixture Model and Differencing

2014 ◽  
Vol 998-999 ◽  
pp. 823-827
Author(s):  
Wen Zhao Zhang ◽  
Wei Wu ◽  
Xian Geng Shen
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Moving Body ◽  
Time Periods ◽  
Temporal Differencing

This paper presented a detecting algorithm based on improved Gaussian mixture model, which improved the speed of establishing and updating the model. Whilst, according to the detection requirements in different time periods, using the method of combining temporal differencing and background differencing to detect moving body improved the instantaneity and veracity of moving body detection.

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abdullah Alharbi ◽  
Wajdi Alhakami ◽  
Sami Bourouis ◽  
Fatma Najar ◽  
Nizar Bouguila
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Support Vector ◽  
Learning Framework ◽  
Probabilistic Support ◽  
Discriminative Approach ◽  
Vector Machines

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

Improved object detection method of adaptive Gaussian mixture model

2013 ◽  
Vol 33 (9) ◽  
pp. 2610-2613
Author(s):  
Hongsheng LI ◽  
Yueju XUE ◽  
Xiaolin HUANG ◽  
Ke HUANG ◽  
Jinhui HE
Keyword(s):  
Object Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Gaussian Mixture

A Novel Skin Image Detection Method Based on GMM and GAs

2011 ◽  
Vol 128-129 ◽  
pp. 482-486
Author(s):  
Ke Ming Mao ◽  
Zhi Liang Zhu ◽  
Hui Yan Jiang ◽  
Zhuo Fu Deng
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection Method ◽  
Color Space ◽  
Selection Procedure ◽  
Gaussian Mixture ◽  
Image Detection ◽  
Rgb Color Space ◽  
Roc Test ◽  
New Skin

This paper proposes a new skin image detection method. First, skin pixel histogram in RGB color space is analyzed. Then Gaussian Mixture Model is used to constructed distribution of skin pixels. Second, a Gaussian parameter combination and selection procedure is implemented with Genetic Algorithms, and the optimal Gaussian Mixture Model can be obtained. Experimental results on public database show that our proposed method outperforms the traditional method with ROC test.

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