scholarly journals Image Processing Methods for Quantitative Analysis of Mitochondrial DNA Dynamics

2012 ◽  
Vol s3 ◽  
Author(s):  
Toshiki Matsuo ◽  
Kyohei Nakayama
2017 ◽  
Vol 4 (1) ◽  
pp. 15
Author(s):  
Yuant Tiandho

<p><em>Currently, a porous material has been extensively developed in many areas of applied science and engineering. The characteristics of porous material is most often determined by its porosity. In this paper, we present a quantitative analysis of pores in a material according to image processing methods. An micrograph from electron microscopy (SEM) was analyzed by using Wolfram Mathematica. From our study can be obtained some informations about pore percentage (porosity), pore size,  ratio aspect, and distribution of pore size in the materials.</em></p><p><em><strong>Keywords</strong></em><em>: pore, image processing, Wolfram Mathematica</em></p><p><em><br /></em></p><p><em>Saat ini, material berpori telah dikembangkan secara luas di banyak bidang sains terapan dan teknik. Karakteristik dari material berpori seringkali ditentukan oleh porositasnya. Dalam makalah ini, kami menyajikan menyajikan analisis pori dalam suatu material berdasarkan metode pengolahan citra. Suatu mikrograf dari mikroskopi elektron (SEM) kami analisis dengan menggunakan Wolfram Mathematica. Dari penelitian kami dapat diperoleh beberapa informasi tentang persentase pori (porositas), ukuran pori, aspek rasio, dan distribusi ukuran pori dalam material.</em></p><p><em><strong>Kata kunci</strong></em><em>: pori, pengolahan citra, Wolfram Mathematica</em></p>


Author(s):  
Iza Sazanita Isa ◽  
Mohamad Khairul Faizi Mat Saad ◽  
Muhammad Haris Khusairi Mohmad Kadir ◽  
Ahmad Afifi Ahmad Afandi ◽  
Noor Khairiah A. Karim ◽  
...  

1989 ◽  
Vol 1989 (14B) ◽  
pp. 25-39
Author(s):  
Katsuaki KOIKE ◽  
Hiroyuki ITOH ◽  
Michito OHMI

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Leonid P. Yaroslavsky

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.


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