Image Processing
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.
INTRODUCTION
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
Methods
· Edge-Detection Techniques
· Digital Morphology
· Texture
· Thinning and Skeletonization Algorithms
GREY-LEVEL SEGMENTATION TECHNIQUES
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 shape
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.
INTRODUCTION
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
Methods
· Edge-Detection Techniques
· Digital Morphology
· Texture
· Thinning and Skeletonization Algorithms
GREY-LEVEL SEGMENTATION TECHNIQUES
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 shape