Image Processing

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Abstract – Image Processing Algorithms are the basis for
Image Computer Analysis and Machine Vision. Employing a
theoretical foundation – Image Algebra – and powerful
development tools – Visual C++, Visual Fortran, Visual
Basic, and Visual Java – high-level and efficient Computer Vision Techniques have been developed. This paper
analyzes different Image Processing Algorithms by
classifying them in logical groups. In addition, specific
methods are presented illustrating the application of such
techniques to the real-world images. In most cases more
than one method is used. This allows a basis for comparison
of different methods as advantageous features as well as
negative characteristics of each technique is delineated.
The Image Algebra [10] forms a solid theoretical
foundation to implement computer vision and image
processing algorithms. With the use of very efficient and
reliable high-level computer languages such as C/C++,
Fortran 90, and Java, innumerable image processing and
machine vision algorithms have been written and optimized.
All this code written and compiled has become a powerful
tool available for researchers, scientists and engineers,
which further accelerated the investigation process and
incremented the accuracy of the final results.
The discussion of the Basic Machine Vision and
Image Processing Algorithms should be divided in five
major groups [11]:
· Grey-Level Segmentation or Thresholding
· Edge-Detection Techniques
· Digital Morphology
· Texture
· Thinning and Skeletonization Algorithms
Thresholding or grey-level segmentation is an essential
concept related with image processing and machine vision.
Thresholding is a conversion between a grey-level image
and a bilevel image. Bilevel image is a monochrome image
only composed by black and white pixels. It should contain
the most essential information of the image (i.e., number,
position and...
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