الفهرس 
CHAPTER (1) INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
This chapter has presented a robust method for identification of landmines
from acoustic images using SVM based on MFCC and the sine, cosine and
wavelet transform techniques. Firstly, the MFCCs feature coefficients are
extracted from the images after transformation to 1-D signals by three different
ways (lexicographic transformation or block by block scan or spiral scan) and the
DST, DCT and DWT of these signals and/ or concatenation of each with the
original image signals to form a large vector called a feature vector for each
image. So for all images, we have a feature matrix. Secondly, this feature matrix
is used to train an SVM classifier. Experimental results have proven that the
proposed method is useful for feature matching of images as a new application
for MFCC technique as it is always used for speech recognition. The best
performance is achieved by features extracted from the DST of images
contaminated by AWGN, features extracted from the image signals plus the DCT
of image signals contaminated by impulsive noise and features extracted from the
image signals plus the DST of image signals contaminated by speckle noise. Also
SVM takes less time than that of ANN except in the case of DCT and DST of
signals.