How to cite (IEEE): A. Mohammed Baqer, & T. Al-Asadi "The Similarity of The Parts of Iris Texture," JOIV : International Journal on Informatics Visualization, vol. 3, no. 2, , pp. 151 - 156, May. 2019. https://doi.org/10.30630/joiv.3.2.238

How to cite (APA): Mohammed Baqer, A., & Al-Asadi, T. (2019). The Similarity of The Parts of Iris Texture. JOIV : International Journal on Informatics Visualization, 3(2), 151 - 156. https://doi.org/10.30630/joiv.3.2.238

How to cite (Chicago): Mohammed Baqer, Ali Abdul Azeez, AND Al-Asadi, Tawfiq A.. "The Similarity of The Parts of Iris Texture" JOIV : International Journal on Informatics Visualization [Online], Volume 3 Number 2 (11 May 2019) https://doi.org/10.30630/joiv.3.2.238

How to cite (Vancouver): Mohammed Baqer A, & Al-Asadi T . The Similarity of The Parts of Iris Texture. JOIV : International Journal on Informatics Visualization [Online]. 2019 May; 3(2):151 - 156. https://doi.org/10.30630/joiv.3.2.238

How to cite (Harvard): Mohammed Baqer, A., & Al-Asadi, T. ,2019. The Similarity of The Parts of Iris Texture. JOIV : International Journal on Informatics Visualization, [Online] 3(2), pp. 151 - 156. https://doi.org/10.30630/joiv.3.2.238

How to cite (MLA8): Mohammed Baqer, Ali Abdul Azeez, & Tawfiq A. Al-Asadi. "The Similarity of The Parts of Iris Texture." JOIV : International Journal on Informatics Visualization [Online], 3.2 (2019): 151 - 156. Web. 3 Oct. 2023 , https://doi.org/10.30630/joiv.3.2.238

BibTex Citation Data :

@article{JOIV238,
    	author = {Ali Abdul Azeez Mohammed Baqer and Tawfiq A. Al-Asadi},
    	title = {The Similarity of The Parts of Iris Texture},
    	journal = {JOIV : International Journal on Informatics Visualization},
    	volume = {3},
    	number = {2},
    	year = {2019},
    	keywords = {pupil; similarity; iris; watershed; GLCM.},
    	abstract = {In the proposed search, new technique for calculating the similarity among parts related to iris images for the selected person and among iris images will be suggested by dividing iris texture into pre decided number of parts and calculating some features of each one of these parts as a texture then calculating degree of similarity among specific part and all other parts of the same iris and with some irises in database according to some accepted rules. The proposed method divided iris texture into eight parts with (45 degrees) for each part, the features of GLCM algorithm will be calculated for parts of iris image then the distance or similarity between specific and required iris with other irises that will be stored in database will be calculated and finally, the similarity calculated between each part (with 45 degrees) of iris and parts of irises stored in database. The proposed system applied some suitable techniques for localized the pupil of the iris image by using watershed segmentation method by drawing arrows between each pixels and 8-nighbouring pixels according to occurrence of some suitable feature then deleting all arrows from selected pixels if another feature doesn’t available in this pixels, this proposed system used for reducing database size for each registered person depending on extracted features from some parts of another person or from extracted features from one part of iris image.},
        	issn = {2549-9904},    	pages = {151--156},    	doi = {10.30630/joiv.3.2.238},
        	url = {http://joiv.org/index.php/joiv/article/view/238}
    }
    

Refworks Citation Data :

@article{{JOIV}{238}, author = {Mohammed Baqer, A., Al-Asadi, T.}, title = {The Similarity of The Parts of Iris Texture}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {3}, number = {2}, year = {2019}, doi = {10.30630/joiv.3.2.238}, url = {} }