Keywords : Co-Occurrence Matrix

Proposed Algorithm For Using GLCM Properties To Distinguishing Geometric Shapes

Kifaa Thanoon

AL-Rafidain Journal of Computer Sciences and Mathematics, 2019, Volume 13, Issue 1, Pages 32-47
DOI: 10.33899/csmj.2020.163501

In this research, an algorithm was used to look at the characteristics of a set of images for geometric shapes and then to classify them into totals based on four characteristics obtained from the co-occurrence matrix (energy, contrast, correlation and homogeneity).
Studying the above four characteristics in detail and then presenting a complete presentation on the extent of their effect on the distinctive characteristics of the geometrical shapes. The adopted algorithm shows that the above four qualities can be new features of geometric shapes in digital images.
The results of the practical application of the proposed algorithm show that the three features of homogeneity, energy, and contrast give a topical distinction to the shape, but the correlation property is weak in the distinction of shape.
The algorithm was programmed using MATLAB R2010a for Windows 7 operating system on the computer that has the following specifications: (Processor Intel (R) Core (TM) i5, CPU 640 M & 2.53 GHZ, RAM 6GB).

Proposed Method for Tracking Moon Phases using the Co-Occurrence Matrix

Abeer A. Thanoon

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 91-104
DOI: 10.33899/csmj.2010.163929

The study Proposes a method to detect the appearance of moon phase in a video serial (AVI) . The proposed algorithm  of photographic and videoing treatment presents a method to trace the appearance of moon phases through the analysis of image texture using Co-Occurrence matrix after reading the video file followed by the representation of texture features in the form of histogram followed by the segmentation of image depending on the values of histogram to obtain the detection of the target , i.e. , the moon in order to trace the appearance of moon phases within a video serial and then know the area of the lighted part of the moon surface via sun rays through which the geometric shape of the lighted area of the moon along the video serial can be estimated in each stage . then the moon phase may be expected as it is a ratio of the estimated area of the geometric shape in relation to the total area of the circular disc of the full moon . the purposed method can be applied whenever a video film of the moon is available.

Skin Classification Based on Co-occurance Matrix

khalil I. Alsaif; Shaimaa M. Mohi Al-Deen

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 41-50
DOI: 10.33899/csmj.2010.163908

In this paper an algorithm will be achieved to look for the properties of the skin for group then try to classify the skin of the group depending on the four properties (energy, contrast, correlation and homogeneity).
Studying the four above properties in details then gave whole view about their effect on skin feature extraction. The applied algorithm shows that the four above properties can be extracted as features for personal skin.
            The experimental results of the proposed algorithm shows that the energy gave high recognition properties comparing with the remaining properties.