Open AccessMathematical Model for Single and Multiple Object Extraction
Author(s) -
Amna Shujahuddin,
Muhammad Salim Khan,
Haider Ali
Publication year - 2021
Publication title -
the punjab university journal of mathematics
Language(s) - English
Resource type - Journals
ISSN - 1016-2526
DOI - 10.52280/pujm.2021.530603
Subject(s) - artificial intelligence , noise (video) , computer science , outlier , segmentation , image segmentation , kernel (algebra) , computer vision , pattern recognition (psychology) , function (biology) , image (mathematics) , shadow (psychology) , image processing , distortion (music) , algorithm , mathematics , combinatorics , evolutionary biology , biology , psychology , amplifier , computer network , bandwidth (computing) , psychotherapist
In the image processing, noise is referred to as the visual distortion. This undesirable by-product may be captured inan image due to unpreventable assorted reasons. The interferenceof natural phenomena and technical problem, such as small sensorsize, long exposure time, low ISO, shadow noise etc., can polluteimage. The presence of noise images affects image processing outputs that include segmentation. Segmentation for noisy images isthe major concern. To tackle this issue, we propose a modernisticmodel that is able neutralize the negative effects of outlier usingthe characteristic of kernel function by different approaches suchas linear approach and quadratic approach for global segmentation. Moreover the weight function is used for local segmentationof noisy images. Comparing with classical models, the proposedtechnique shows robust performance. In comparison with the wellknown models such as Chan-Vese (CV) model , Yongfei Wu andChuanjiang He (Wu-He) model and Chunming Li (Li) model weconclude that performance of our new model is much better.