使用深度学习的图像分割:综述

Hana Sharif, Faisal Rehman, Amina Rida, Aman Sharif
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引用次数: 7

摘要

在当今的技术时代,图像分割是计算机视觉和图像处理应用的基础和重要组成部分。这些应用包括医学图像分析、图像压缩、监控、安全录像等。研究人员已经提出并发展了各种模型、理论和算法在这个不断发展的图像分割领域。由于计算机视觉应用的广泛成功,不久前完成了以使用深度学习算法开发和可用性图像分割为目标的工作。在本研究中,正在对图像分割的文献进行详细的综述,其中涵盖了撰写本综述时研究人员所做的大量工作。本文回顾了过去研究人员提出的模型之间的异同。除了相似之处,我们还讨论了在深度学习算法的保护下模型的强度和挑战。最后,在回顾前人研究成果的同时,对今后的工作进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation of Images Using Deep Learning: A Survey
In this present era of technology, segmentation of image is a fundamental and significant component of computer vision and image processing applications. These applications include medical image analysis, compression of images, surveillance, security footage, and many others. Researchers have proposed and developed various models, theories, and algorithms in this advancing field of image segmentation. Due to the success of a wide range of computer vision applications, work whose goal was to use deep learning algorithms for the development and usability of image segmentation was completed not long ago. In this study, a detailed review of the literature on image segmentation is being written, which covers a vast and extensive amount of work done by researchers at the time of writing this review. This review investigates the similarity and differences between the proposed models by researchers in the past. Along with similarities, we have also discussed the strength and challenges of the model under the umbrella of deep learning algorithms. In the end, future work has been discussed along with keeping in view the past work of researchers.
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