A survey on instance segmentation: state of the art
A survey on instance segmentation: state of the art
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DOI：
10.1007/s13735-020-00195-x
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发表时间：
2020-07-03
影响因子：
5.6
通讯作者：
Bhat, Ghulam Mohiuddin
中科院分区：
文献类型：
--
作者：
Hafiz, Abdul Mueed;Bhat, Ghulam Mohiuddin
对象检测或定位是从粗略到精细数字图像推理的渐进步骤。它不仅提供了图像对象的类别，还提供了已分类的图像对象的位置。该位置以边界框或质心的形式给出。语义分割通过预测输入图像中每个像素的标签来提供精细的推理。每个像素根据其所在的对象类进行标记。进一步推动这种发展，实例分割为属于同一类的对象的单独实例提供了不同的标签。因此，实例分割可以被定义为同时解决对象检测和语义分割问题的技术。在这篇关于实例分割的调查论文中，讨论了它的背景、问题、技术、演变、流行数据集、最新的相关工作和未来的范围。该论文为那些想要在实例分割领域进行研究的人提供了有价值的信息。
Object detection or localization is an incremental step in progression from coarse to fine digital image inference. It not only provides the classes of the image objects, but also provides the location of the image objects which have been classified. The location is given in the form of bounding boxes or centroids. Semantic segmentation gives fine inference by predicting labels for every pixel in the input image. Each pixel is labelled according to the object class within which it is enclosed. Furthering this evolution, instance segmentation gives different labels for separate instances of objects belonging to the same class. Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In this survey paper on instance segmentation, its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. The paper provides valuable information for those who want to do research in the field of instance segmentation.