Abstract:
Image identification plays a great role in industrial, remote sensing, medical and military applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to classify the texture of human member image then identify ...
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Abstract:
Image identification plays a great role in industrial, remote sensing, medical and military applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to classify the texture of human member image then identify whether the member is infected or not. The program has the ability of determining which part of that member is infected depending on the comparison between the healthy member image stored in advance with a test image.
The first step is to make approximation to the image using wavelet network (Wavenet) technique. Through this technique we shall get an approximated image with reduced data. In addition, we shall get implicit information to that image. The second step is to subdivide the resultant image from the first step into 16 equally subparts then deal with each subpart as a unique image.
Finally, in the third step, the minimum distance (Mahalanobias Distance) approach is employed for subpart identification. All programs are written using MATLAB VER. 6.5 package.
Abstract:
Image identification plays a great role in industrial, remote sensing, and military
applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to identify the color image
depending on the distribution of the monochrome ...
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Abstract:
Image identification plays a great role in industrial, remote sensing, and military
applications. It is concerned with the generation of a signature to the image.
This work proposes a dynamic program (use Neural Network) to identify the color image
depending on the distribution of the monochrome colors (red, green, and blue) in the same
image to make image signature accordingly, which is represented by a values named power
spectrum. The first step is to analyze the three-band monochrome image (color image) to
Red, Green and Blue image, then deal with each image as a grey scale one which is
represented as a 2-D matrix. The second step is to make Fourier Transform to each grey
scale image in order to extract the implicit information in that image. The calculations of 2-
D Power Spectrum for each image have been done to construct the final feature vector for
each one. Finally, in the third step, and in order to handle problems of large input
dimensions, a multilayer perceptron Neural Network has been used with two hidden layers.
The input of the Neural Network structure is the final feature vectors which are obtained
from the previous step. All programs are written using MATLAP VER. 6.5 programming
language.