Abstract
The scanning electron microscope (SEM) remains a main tool for semiconductor and polymer physics but TEM and AFM are increasingly used for minimum size features which called nanomaterials. In addition some physical properties such as microhardness, grain boundaries and domain structure are observed from optical and polarizing microscope which gives poor information and consequentially the error probability of discussion will be high. Thus it is natural to squeeze out every possible bit of resolution in the SEM, optical and polarizing microscopes for the materials under test. In our paper we will tackling this problem using different image processing techniques to get more clarify and sufficient information. In the suggested paper we will obtain set of images for prepared samples under different conditions and with different physical properties. These images will be analyzed using the above mentioned technique which starting by converting the prepared samples images (gray scale or colored images) to data file (*.dat) in two dimensional using programming. The 2D data will convert to 3D data file using FORTRAN programming. All images will subject to the generate filter algorithm for 3D data file. After filtering the 3D data file we can establish histogram, contours and 3D surface to analysis the image. Another technique will be prepared using Visual FORTRAN for steepest descent algorithm (SDA) which gives the vector map for the obtained data. Finally the depth from one single still image will be created and determine using OpenGL library under Visual C++ language, as well as, perform texture mapping. The quality of filtering depends on the way the data is incorporated into the model. Data should be treated carefully. From our paper we can analysis any part from any image without reanalysis the image, all size of the image as in this paper we take three samples with different size (256 * 256), (400 * 400), (510 * 510), this method decrees the cost of hardware and sample.
Highlights
Digital image processing analysis and computer visions have exhibited an impressive growth in the past decade in terms of both theoretical development and applications
Interpretation of the image includes the use of brightness information, and the identification of features in the image [1] and [2].Spatial enhancement is the mathematical processing of the image pixel data to emphasize spatial relationships
In this paper we introduce method for converting the image formats to digital images
Summary
Digital image processing analysis and computer visions have exhibited an impressive growth in the past decade in terms of both theoretical development and applications They constitute a leading technology in a number of very important areas, for example in digital telecommunication, broadcasting medical imaging, multimedia systems, biology, material sciences, Robotics and manufacturing, intelligent sensing systems, remote sensing, graphic arts and printing. It applies the same spectral transformation to all pixels with a given gray scale in the image. Step 3: Perform a single parameter minimization of ( v ) f ( p k v s k ) on the interval [ 0,b ], where b is large This will produce a value v hmin where a local minimum for ( v ) .
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