Computer vision is an influential area in which methodologies are generated to analyze and know about the charactristics and construction of a digital image and output is some meaningful information. Image processing comprises five main branches i.e image segmentation, image denoising, image registration, image inpainting and image deblurring. Image segmentation is our focus research work in context of fuzzy sets theory. The pivotal element to fuzzy sets [11] is fuzzy membership V, which acts like region descriptor, must satisfy the restriction Level set method (LSM) [9] is used, which is responsible to distribute and allogate the evolution curve C, which is a better way to carry out image segmentation process. In our research work we developed a model for segmenting images with inhomogeneous intensity multi objects background having maximum, minimum, average intensities. For such achievement we changed Krinidis and Chartiz [13] fitting term by linear term in fuzzy setup. Experimental result of our model justify that our model will show better performance in those images which are suffering from intensity inhomogeneity multi objects.
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Rahman Ullah,
rahmanktk344@gmail.com,
Department of Basic Sciences, University of Engineering and Technology Peshawar,
Pakistan.
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Noor Badshah,
noor2knoor@gmail.com,
Department of Basic Sciences, University of Engineering and Technology Peshawar,
Pakistan.
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Mati Ullah,
,
Department of Basic Sciences, University of Engineering and Technology Peshawar,
Pakistan.
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Muhammad Arif,
rfktk1@gmail.com,
Department of Basic Sciences, University of Engineering and Technology Peshawar,
Pakistan.
Rahman Ullah Noor Badshah Mati Ullah Muhammad Arif “Segmentation of Images with Inhomogeneous Intensity Multi-Objects” International Journal of Engineering Works Vol. 8 Issue 03 PP. 112-117 March 2021 https://doi.org/10.34259/ijew.21.803112117.
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, Mati Ullah, Muhammad Arif
