Presentation in Aircraft Satellite Image Identification Using Bayesian Decision Theory And Moment Invariants Feature Extraction Dickson Gichaga Wambaa Supervised By Professor Elijah Mwangi University Of Nairobi Electrical And Information Engineering Dept. • 9 th May 2012 IEK Presentation
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Presentation in Aircraft Satellite Image Identification Using Bayesian
Decision Theory And Moment Invariants Feature ExtractionDickson Gichaga Wambaa
Supervised By Professor Elijah MwangiUniversity Of Nairobi
Electrical And Information Engineering Dept.
• 9th May 2012 IEK Presentation
OUTLINE
Introduction
Statistical Classification
Satellite images Denoising
Results
Conclusion
References
All aircraft are built with the same basic elements: Wings Engine(s) Fuselage Mechanical Controls Tail assembly. The differences of these elements distinguish one aircraft type from another and therefore its identification.
STAGES OF STATISTICAL PATTERN RECOGNITION
• PROBLEM FORMULATION• DATA COLLECTION
AND EXAMINATION• FEATURE
SELECTION OR EXTRACTION
• CLUSTERING• DISCRIMINATION• ASSESSMENT OF
RESULTS• INTERPRETATION
Classification ONE• There are two main
divisions of classification: • Supervised•unsupervised
SUPERVISED CLASSIFICATION
• BAYES CLASSIFICATION IS SELECTED SINCE IT IS POSSIBLE TO HAVE EXTREMELY HIGH VALUES IN ITS OPTIMISATION.
A decision rule partitions the measurement space into C regions.
Preprocessing
PREPROCESSING
IMAGE ACQUISITION
IMAGE ENHANCEMENT
IMAGE BINARIZATION
AND THRESHOLDING
FEATURES EXTRACTION
NOISEIMAGES ARE CONTAMINATED BY
NOISE THROUGH– IMPERFECT INSTRUMENTS– PROBLEMS WITH DATA ACQUISITION PROCESS– NATURAL PHENOMENA INTERFERENCE– TRANSMISSION ERRORS
SPECKLE NOISE(SPKN)
• THE TYPE OF NOISE FOUND IN SATELLITE IMAGES IS SPECKLE NOISE AND THIS DETERMINES THE ALGORITHM USED IN DENOISING.
Speckle Noise (SPKN) 2
• This is a multiplicative noise. The distribution noise can be expressed by:
J = I + n*I • Where, J is the distribution speckle
noise image, I is the input image and n is the uniform noise image.
CHOICE OF FILTER
FILTERING CONSISTS OF MOVING A WINDOW OVER EACH PIXEL OF AN IMAGE AND TO APPLY A MATHEMATICAL FUNCTION TO ACHIEVE A SMOOTHING EFFECT.
CHOICE OF FILTER II
• THE MATHEMATICAL FUNCTION DETERMINES THE FILTER TYPE.• MEAN FILTER-AVERAGES THE
WINDOW PIXELS• MEDIAN FILTER-CALCULATES THE
MEDIAN PIXEL
CHOICE OF FILTER II
• LEE-SIGMA AND LEE FILTERS-USE STATISTICAL DISTRIBUTION OF PIXELS IN THE WINDOW
• LOCAL REGION FILTER-COMPARES THE VARIANCES OF WINDOW REGIONS.
• THE FROST FILTER REPLACES THE PIXEL OF INTEREST WITH A WEIGHTED SUM OF THE VALUES WITHIN THE NxN MOVING WINDOW AND ASSUMES A MULTIPLICATIVE NOISE AND STATIONARY NOISE STATISTICS.
LEE FILTER
Adaptive Lee filter converts the multiplicative model into an additive one.
• COMBINING MOMENTS FEATURES EXTRACTION WITH BAYESIAN CLASSIFICATION WHILE USING LEE FILTERS IN PREPROCESSING
• INCREASES THE CHANCES OF CORRECT IDENTIFICATION AS COMPARED TO NON USE OF THE FILTERS
• USE OF OTHER TYPES OF FILTERS THIS IS SEEN BY THE INCREASE OF THE POSTERIOR PROBABILITY VALUES.
References
• [1] Richard O. Duda,Peter E. Hart and David G. Stork.Pattern Classification 2nd edition John Wiley and Sons,US,2007
• [2] Rafael C. Gonzalez,Richard E. Woods and Steven L. Eddins . Digital image processing using matlab 2nd edition Pearson/Prentice Hall,US,2004
• [3] William K. Pratt. Digital image processing 4th edition John Wiley,US,2007• [4] Anil K. Jain. Fundamentals of Digital Image
Processing Prentice Hall,US,1989
References
• [5] Wei Cao, Shaoliang Meng, “Imaging systems and Techniques”,IEEE International Workshop, IST.2009.5071625,pp 164-167, Shenzhen, 2009
• [6] Bouguila.N, Elguebaly.T , “A Bayesian approach for texture images classification and retrieval”,International Conference on Multimedia Computing and Systems, ICMS.2011.5945719,pp 1-6,Canada, 2011
References
• [7] Dixit. M, Rasiwasia. N, Vasconcelos. N, “Adapted Gaussian models for image classification” ,2011 IEEE Conference on Computer Vision and Pattern, CVPR.2011.5995674, pp 937-943, USA,2011
• [8] Mukesh C. Motwani,Mukesh C. Gadiya,Rakhi C. Motwani, Frederick C. Harris Jr. , “Survey Of Image Denoising Techniques”,University of Nevada Reno, US, 2001