Detecting Known Objects in a Noisy Scene Using Generalized Likelihood Ratio Test

Qi Chuan Tian; Jie Wen

doi:10.4028/www.scientific.net/AMR.518-523.3843

Paper Titles

The Analysis of Vibration Fault Features and Vibration Mechanism Caused by Rotating Machinery Loosening
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The Influence of Users' Behavioral Characteristics on Soundscape in the Waiting Halls of Railway Stations
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Analytical Sensitivity of Acoustic Power Radiated from a Closed Structure Based on the Theory of Acoustic Radiation Modes
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Application of BP Neural Network in Traffic Noise Prediction
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Detecting Known Objects in a Noisy Scene Using Generalized Likelihood Ratio Test
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Improved EEMD in the Application of Signal Singularity Detection
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Model and Application on Displacement Calculation of Abutment by Considering Earthquake Effects
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Research on Free Damping Treatment for a Marine Life Hoist of the Helicopter
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Study on Filling Fraction of Bandgap Lowest Starting Frequency of one Dimension Phononic Crystals
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 518-523Detecting Known Objects in a Noisy Scene Using...

Detecting Known Objects in a Noisy Scene Using Generalized Likelihood Ratio Test

Abstract:

According to the problem of the identification and localization of a known object in a scene, satisfied detection results can not be achieved using traditional detectors for images in photon-limited noise, an algorithm named Generalized Likelihood Ratio Test (GLRT) was derived for detecting known objects in a noisy scene. We used this algorithm to evaluate the existence of tiger in photons-limited images. Results show that the GLRT algorithm is effectiveness in detecting and localizing a known object embedded in a background image from photon-limited observations.

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Periodical:

Advanced Materials Research (Volumes 518-523)

Pages:

3843-3846

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.518-523.3843

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Online since:

May 2012

Authors:

Qi Chuan Tian, Jie Wen

Keywords:

Generalized Likelihood Ratio Test, Object Detection in Noisy Scene, Photon-Limited

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References

[1] Jinho Choi. Data detection with imperfect CSI using averaged likelihood function. IEEE transactions on wireless communications, 2008, 7(11): 4117-4121

DOI: 10.1109/t-wc.2008.070627

Google Scholar

[2] Shot noise: Photon-noise.jpg, Wikipedia, http://en.wikipedia.org/wiki/File: Photon-noise.jpg

Google Scholar

[3] Steven M. Kay. Fundamentals of Statistical Signal Processing. Detection Theory. Upper Saddle River, New Jersey; Prentice Hall, (1998)

Google Scholar

[4] Song Wang, Stewart Ethier. A generalized likelihood ratio test to identify differentially expressed genes from microarray data.Bioinformatics, 2004, 20(1) , pp.100-104

DOI: 10.1093/bioinformatics/btg384

Google Scholar

[5] Jianqing Fan,Chunming Zhang and Jian Zhang. Generalized likelihood ratio statistics and WILKS phenomenon, The Annals of Statistics, 2001, 29(1), pp.153-193

DOI: 10.1214/aos/996986505

Google Scholar

[6] De Maio, A.; De Nicola, S.; Yongwei Huang; Shuzhong Zhang; Farina, A. Adaptive Detection and Estimation in the Presence of Useful Signal and Interference Mismatches, Signal Processing, IEEE transactions, 2009, 57(2): 436-450

DOI: 10.1109/tsp.2008.2008249

Google Scholar

[7] Abu-Naser, Ahmad; Galatsanos, Nikolas P.; Wernick, Miles N. Generalized likelihood ratio test based algorithms for object recognition in photon-limited images. Proceedings of International Conference on Image Processing, ICIP, 2005(3), pp.525-528.

DOI: 10.1109/icip.2005.1530444

Google Scholar

[8] Abu-Naser, Ahmad; Galatsanos, Nikolas P.; Wernick, Miles N. Methods to detect objects in photon-limited images, Journal of the Optical Society of America A: Optics and Image Science, and Vision, 2006,23(2), pp.272-278

DOI: 10.1364/josaa.23.000272

Google Scholar