Deep Learning Models for Image Segmentation

Sanskruti Patel
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引用次数: 5

Abstract

Artificial Intelligence and deep learning models have evolved rapidly in the last decade and successfully applied to face recognition, autonomous driving, satellite imaging, robotics, and many more. Computer vision tasks often require adequate segmentation of an image that helps to understand the patterns and information. The adequate segmentation makes the analysis of each part of an image easier. Traditional segmentation techniques are often applied for image segmentation, but they are less efficient than deep learning techniques. Using deep learning approaches, it is possible to obtain hierarchical feature representations directly from the images, and hence, it eliminates the requirement of handcrafted features. This paper covers the fundamentals of image segmentation and deep learning, deep learning models for image segmentation, some successful implementations of deep learning models for image segmentation, and available open and benchmark datasets for image segmentation tasks.
图像分割的深度学习模型
人工智能和深度学习模型在过去十年中发展迅速,并成功应用于人脸识别、自动驾驶、卫星成像、机器人等领域。计算机视觉任务通常需要对图像进行适当的分割,以帮助理解模式和信息。适当的分割使分析图像的每个部分更容易。传统的分割技术通常用于图像分割,但其效率低于深度学习技术。使用深度学习方法,可以直接从图像中获得分层特征表示,因此,它消除了手工制作特征的要求。本文涵盖了图像分割和深度学习的基础,图像分割的深度学习模型,图像分割的一些成功的深度学习模型的实现,以及用于图像分割任务的可用开放和基准数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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