Picture Segmentation using changing Artifacts Identification and Bias Modification
Picture segmentation is a crucial task in computer vision that involves dividing an image into multiple regions or segments based on various criteria, such as color, texture, or edge characteristics. Changing artifacts identification involves identifying and removing artifacts that are present in the image and affecting the accuracy of segmentation. Artifacts can be caused by various factors, such as noise, lighting conditions, or image compression. Identifying and removing these artifacts can improve the accuracy of segmentation algorithms. Bias modification involves adjusting the biases present in segmentation algorithms to improve their accuracy. These biases can lead to errors in segmentation, such as over-segmentation or under-segmentation. Bias modification can involve adjusting the algorithm's parameters or using different training data to improve its performance. The use of changing artifacts identification and bias modification can significantly improve the accuracy of picture segmentation. By identifying and removing artifacts and adjusting biases, segmentation algorithms can more accurately identify and classify objects in an image, leading to better object detection and classification. This model is expected to be useful for damaged images and applications where artifacts and bias are actual features of interest, such as lesion detection(problem within medical imaging analysis) and bias field correction in medical imaging, e.g., Magnetic resonance imaging (MRI).
Email Address of Submitting Authorpranachilukuri22@gmail.com
Submitting Author's InstitutionKakatiya Institute of technology and science
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