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|Title: ||Binary Image Segmentation through the Carl Friedrich Gauss Equation|
|Authors: ||Oppong-Twum, Francis|
Hayfron-Acquah, J. B.
|Keywords: ||Twum-Acquah algorithm|
Carl Friedrich Gauss equation
|Issue Date: ||May-2018|
|Publisher: ||International Journal of Computer Applications|
|Citation: ||International Journal of Computer Applications,Volume 179 – No. 41|
|Abstract: ||Binary image segmentation is a technique that enables an
image to be divided into several related portions. In this
research, the iterative, Otsu, multiple, adaptive and global
algorithms were reviewed to evaluate the importance of the
techniques and challenges that limit their usage.
The study utilized the Carl Friedrich Gauss equation to
suppress the impact of noisy pixels. The matrix generated was
converted into integers to generate a histogram. An arbitrary
pixel is selected from the histogram as a threshold to partition
the image into two classes. The threshold that generates the
minimum variance from the classes is then multiplied by the
optimization constant which ranges from 0.1 to 1, and the
computed value is used for the segmentation process.
An improved Otsu Algorithm based on the Carl’s Friedrich
Gauss equation was evaluated with the Otsu, multiple,
adaptive, and global algorithms. The signal to noise ratio that
defines the sensitivity of a segmentation algorithm, and the
running time that specifies the quantum of time required by an
algorithm to execute were used as the metrics of performance.
The experiments conducted using MATLAB and the Berkeley
Image Segmentation Dataset was as follows:
The first experiment consisted of five noise free images. In
the experiment, the adaptive obtained the highest sensitivity
rating of 8.890dB. This was followed by this studies proposed
Twum-Acquah algorithm at 5.623dB. The worst performance
was recorded in the global at 2.367dB.
In the second experiment that consisted of noisy images, the
proposed Twum-Acquah algorithm obtained the highest
performance rating of 4.444dB, while the Adaptive which was
at the bottom of the evaluation scored 0.851dB.
In terms of the running time, the fastest algorithms were
observed in the global, Otsu and the multiple with a rating of
1.103, 1.264 and 1.392 seconds respectively, while the
slowest was recorded in the Adaptive at 129.479 seconds.|
|Description: ||This article is published in International Journal of Computer Applications and also available at DOI: 10.5120/ijca2018916969|
|Appears in Collections:||College of Science|
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