stdClass Object
(
    [id] => 7060
    [paper_index] => 202205-01-010408
    [title] => ANALYSIS ON THE EFFECT OF A GAUSSIAN NOISE IN IMAGE FILTERING AND SEGREGATION
    [description] => 
    [author] => Shivshankar Joshi , Avinash Singh, Sateesh Kumar Shivhare
    [googlescholar] => https://scholar.google.co.in/scholar?q=eprajournals.com&hl=en&scisbd=2&as_sdt=0,5
    [doi] => 
    [year] => 2022
    [month] => May
    [volume] => 8
    [issue] => 5
    [file] => 1034pm_56.EPRA JOURNALS 10408.pdf
    [abstract] => The effects of convolution of a Gaussian function with an image are investigated in this paper on both a qualitative and quantitative level. This paper studies a methodology of segmentation utilising Gaussian blurring in addition to evaluating the generally known Gaussian-blur in image filtering. Noise is an unavoidable part of the acquisition process. As a result, knowing the impacts of a filtering technique is critical for selecting the suitable technique to effectively filter the image, as the segmentation process can be costly and time-consuming. It’s usually preferable to have an automatic segmentation method that saves time and human labour. In order to analyse the impacts of the convolution in a quantifiable method, we chose a Quality Index to measure the filtering properties. The Gaussian Blur approach should be used in photographs with a lot of noise and a small variance Gaussian function, whereas a higher variance Gaussian function should be used in images with a lot of noise and a large variance Gaussian function.
    [keywords] => 
    [doj] => 2022-06-02
    [hit] => 1447
    [status] => y
    [award_status] => P
    [orderr] => 56
    [journal_id] => 1
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Multidisciplinary Research (IJMR)
    [short_code] => IJMR
    [eissn] =>  2455-3662 (Online)
    [pissn] => - --
    [home_page_wrapper] => images/products_image/11.IJMR.png
)
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